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Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction (2304.14922v3)
Published 24 Apr 2023 in eess.SP, cs.AI, and cs.LG
Abstract: Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.
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[2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. 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[2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Litt, B., Echauz, J.: Prediction of epileptic seizures. Neurology 1, 22–30 (2002) Nguyen and T’ellez Zenteno [2009] Nguyen, R., T’ellez Zenteno, J.F.: Injuries in epilepsy: a review of its prevalence, risk factors, type of injuries and prevention. Neurology International 1 (2009) Ridsdale et al. [2011] Ridsdale, L., Charlton, J., Ashworth, M., Richardson, M.P., Gulliford, M.C.: Epilepsy mortality and risk factors for death in epilepsy: a population-based study. Br J Gen Pract. 61, 271–278 (2011) Fisher [2000] Fisher, R.S.: Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy & Behavior 1, 9–14 (2000) Kuhlmann et al. [2018] Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Nguyen, R., T’ellez Zenteno, J.F.: Injuries in epilepsy: a review of its prevalence, risk factors, type of injuries and prevention. Neurology International 1 (2009) Ridsdale et al. [2011] Ridsdale, L., Charlton, J., Ashworth, M., Richardson, M.P., Gulliford, M.C.: Epilepsy mortality and risk factors for death in epilepsy: a population-based study. Br J Gen Pract. 61, 271–278 (2011) Fisher [2000] Fisher, R.S.: Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy & Behavior 1, 9–14 (2000) Kuhlmann et al. [2018] Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. 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[2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. 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[2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ridsdale, L., Charlton, J., Ashworth, M., Richardson, M.P., Gulliford, M.C.: Epilepsy mortality and risk factors for death in epilepsy: a population-based study. Br J Gen Pract. 61, 271–278 (2011) Fisher [2000] Fisher, R.S.: Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy & Behavior 1, 9–14 (2000) Kuhlmann et al. [2018] Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fisher, R.S.: Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy & Behavior 1, 9–14 (2000) Kuhlmann et al. [2018] Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. 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International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. 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[2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. 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[2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. 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Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ridsdale, L., Charlton, J., Ashworth, M., Richardson, M.P., Gulliford, M.C.: Epilepsy mortality and risk factors for death in epilepsy: a population-based study. Br J Gen Pract. 61, 271–278 (2011) Fisher [2000] Fisher, R.S.: Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy & Behavior 1, 9–14 (2000) Kuhlmann et al. [2018] Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fisher, R.S.: Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy & Behavior 1, 9–14 (2000) Kuhlmann et al. [2018] Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. 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[2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. 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[2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. 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[2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. 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[2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. 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[2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fisher, R.S.: Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy & Behavior 1, 9–14 (2000) Kuhlmann et al. [2018] Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. 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[2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. 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Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. 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Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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[2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. 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[2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. 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[2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. 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[2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Kuhlmann, L., Lehnertz, K., Richardson, M.P., Schelter, B., Zaveri, H.P.: Seizure prediction — ready for a new era. Nature Reviews Neurology 14, 618–630 (2018) Stacey et al. [2011] Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stacey, W., Le Van Quyen, M., Mormann, F., Schulze-Bonhage, A.: What is the present-day eeg evidence for a preictal state? Epilepsy Research 97, 243–251 (2011) Brinkamann et al. [2016] Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. 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[2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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[2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. 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[2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Brinkamann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P., Pardo, J., Zamora-Martinez, F., Hills, M., Wu, W., Korshunova, I., Cukierski, W., Vite, C., Patterson, E.E., Litt, B., Worrel, G.A.: Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016) Bandarabadi et al. [2015] Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Bandarabadi, M., Rasekhi, J., Teixeira, C.A., Karami, M.R., Dourado, A.: On the proper selection of preictal period for seizure prediction. Epilepsy & Behavior 158-166, 46 (2015) Giannakakis et al. [2014] Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. 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[2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Giannakakis, G., Sakkalis, V., Pediaditis, M., Tsiknakis, M.: Methods for seizure detection and prediction: An overview. Modern Electroencephalographic Assessment Techniques 91, 131–157 (2014) Georgis-Yap et al. [2022] Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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[2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Georgis-Yap, Z., Popovic, M.R., Khan, S.S.: Preictal-interictal classification for seizure prediction. In: The 35th Canadian Conference on Artificial Intelligence (2022) Burrello et al. [2019] Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. 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[2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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[2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Burrello, A., Cavigelli, L., Schindler, K., Benini, L., Rahimi, A.: Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy (2019) Shoeb [2009] Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology (2009) Acharya et al. [2018] Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy & Behavior 88, 251–261 (2018) Natu et al. [2022] Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. 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Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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[2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. 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[2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Natu, M., Bachute, M., Gite, S., Kotecha, K., Vidyarthi, A.: Review on epileptic seizure prediction: machine learning and deep learning approaches. Computational and Mathematical Methods in Medicine 2022 (2022) LeCun et al. [2015] LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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[2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. 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In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015) Teixeira et al. [2014] Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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[2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Teixeira, C.A., Direito, B., Bandarabadi, M., Le Van Quyen, M., Valerrama, M., Schelter, B., Schulze-Bonhage, A., Navarro, V., Sales, F., Dourado, A.: Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Computer Methods and Programs in Biomedicine 114, 324–336 (2014) Fei et al. [2017] Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. 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Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. 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Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fei, K., Wang, W., Yang, Q., Tang, S.: Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249, 290–298 (2017) Mirowski et al. [2009] Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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[2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of eeg synchronization for seizure prediction. Clinical Neurophysiology 120, 1927–1940 (2009) Khan et al. [2018] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Transactions on Biomedical Engineering 65, 2109–2118 (2018) Truong et al. [2018] Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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[2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Ippolito, S., Kavehei, O.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Networks 105, 104–111 (2018) Eberlein et al. [2018] Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. 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In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Eberlein, M., Hildebrand, R., Tetzlaff, R., Hoffmann, N., Kuhlmann, L., Brinkmann, B., M”uller, J.: Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid (2018) Zhang et al. [2020] Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE Journal of Biomedical and Health Informatics 24, 465–474 (2020) Liu et al. [2020] Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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[2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. 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Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O’Brien, T., Perucca, P., Ge, Z.: Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, Australia (2020) Dissanayake et al. [2021] Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Fookes, C.: Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors Journal 21, 9377–9388 (2021) Jana and Mukherjee [2021] Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Jana, R., Mukherjee, I.: Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomedical Signal Processing and Control 68 (2021) Xu et al. [2020] Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Xu, Y., Yang, J., Zhao, S., Wu, H., Sawan, M.: An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Italy (2020) Tsiouris et al. [2018] Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Tsiouris, K.M., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I.: A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Computers in Biology and Medicine 99, 24–37 (2018) Abdelhameed and Bayoumi [2018] Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. 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In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Abdelhameed, A.M., Bayoumi, M.: Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando (2018) Daoud and Bayoumi [2019] Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Daoud, H., Bayoumi, M.A.: Efficient epileptic seizure prediction based on deep learning. IEEE Transactions on Biomedical Circuits and Systems 13, 804–813 (2019) Wei et al. [2019] Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Wei, X., Zhou, L., Zhang, Z., Chen, Z., Zhou, Y.: Early prediction of epileptic seizures using a long-term recurrent convolutional network. Journal of Neuroscience Methods 327 (2019) Usman et al. [2020] Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Usman, S.M., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998–40007 (2020) Hussein et al. [2020] Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. 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[2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Hussein, A., Djandji, M., Mahmoud, R., Dhaybi, M., Hajj, H.M.: Augmenting dl with adversarial training for robust prediction of epilepsy seizures. Journal of the ACM 1 (2020) Prathaban and Balasubramanian [2021] Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. 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[2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. 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Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. 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In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Prathaban, B.P., Balasubramanian, R.: Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Systems with Applications 170 (2021) Ozcan and Erturk [2019] Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Ozcan, A.R., Erturk, S.: Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 2284–2293 (2019) Truong et al. [2019] Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Truong, N.D., Zhou, L., Kavehei, O.: Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin (2019) Sherstinsky [2020] Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Sherstinsky, A.: Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena 404, 132306 (2020) Thill et al. [2021] Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Thill, M., Konen, W., Wang, H., B”ack, T.: Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing 112, 107751 (2021) Stefan Denkovski [2023] Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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[2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Stefan Denkovski, S.S.K. Alex Mihailidis: Temporal shift - multi-objective loss function for improved anomaly fall detection. In: 15thsuperscript15𝑡ℎ15^{th}15 start_POSTSUPERSCRIPT italic_t italic_h end_POSTSUPERSCRIPT Asian Conference on Machine Learning (2023) Khan et al. [2021] Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Khan, S.S., Khoshbakhtian, F., Ashraf, A.B.: Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian Conference on AI (2021) Jacob Nogas and Mihailidis [2018] Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Jacob Nogas, S.S.K., Mihailidis, A.: Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd Workshop on Aging, Rehabilitation and Independent Assisted Living, IJCAI Workshop (2018) Abedi and Khan [2023] Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Abedi, A., Khan, S.S.: Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. Signal, Image and Video Processing (2023) Al-Fahoum and Al-Fraihat [2014] Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices 2014 (2014) Herrmann et al. [2014a] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Herrmann et al. [2014b] Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Herrmann, C.S., Rach, S., Vosskuhl, J., Str”uber, D.: Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topography 27, 438–450 (2014) Fadzal et al. [2012] Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. 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Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. 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[2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Fadzal, C.W.N.F.C.W., Mansor, W., Khuan, L.Y., Zabidi, A.: Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, Malacca, Malaysia (2012) Edakawa et al. [2016] Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Edakawa, K., Yanagisawa, T., Kishima, H., Fukuma, R., Oshino, S., Khoo, H.M., Kobayashi, M., Tanaka, M., Yoshimine, T.: Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Scientific reports 6(1), 1–8 (2016) Huang and Ling [2005] Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
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IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tajani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32, Vancouver (2019) Branco et al. [2016] Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Branco, P., Torgo, L., Ribeiro, R.R.: A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR) 49, 1–50 (2016) Khan et al. [2017] Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Khan, S.S., Karg, M.E., Kulić, D., Hoey, J.: Detecting falls with x-factor hidden markov models. Applied Soft Computing 55, 168–177 (2017) Khan et al. [2023] Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Khan, S.S., Mishra, P.K., Ye, B., Newman, K., Iaboni, A., Mihailidis, A.: Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on Robots and Vision (CRV), pp. 265–272 (2023). IEEE Habashi et al. [2023] Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Habashi, A.G., Azab, A.M., Eldawlatly, S., Aly, G.M.: Generative adversarial networks in eeg analysis: an overview. Journal of NeuroEngineering and Rehabilitation 20(1), 40 (2023) Khan et al. [2021] Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021) Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Khan, S.S., Nogas, J., Mihailidis, A.: Spatio-temporal adversarial learning for detecting unseen falls. Pattern Analysis and Applications 24, 381–391 (2021)
- Zakary Georgis-Yap (1 paper)
- Milos R. Popovic (6 papers)
- Shehroz S. Khan (42 papers)