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An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection (2401.05425v2)

Published 1 Jan 2024 in eess.SP and cs.LG

Abstract: Epilepsy is one of the most common neurological diseases globally (around 50 million people worldwide). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel lightweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing-computing-communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer/mobile phone nearby, where data are uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies.

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References (93)
  1. “Epilepsy,” https://www.who.int/news-room/fact-sheets/detail/epilepsy, accessed: 2023-11-02.
  2. R. Barranco, F. Caputo, A. Molinelli, and F. Ventura, “Review on post-mortem diagnosis in suspected SUDEP: Currently still a difficult task for Forensic Pathologists,” Journal of Forensic and Legal Medicine, vol. 70, p. 101920, Feb. 2020.
  3. B. Blachut, C. Hoppe, R. Surges, C. Elger, and C. Helmstaedter, “Subjective seizure counts by epilepsy clinical drug trial participants are not reliable,” Epilepsy & Behavior: E&B, vol. 67, pp. 122–127, Feb. 2017.
  4. P. F. Prior, R. S. M. Virden, and D. E. Maynard, “An eeg device for monitoring seizure discharges,” Epilepsia, vol. 14, 1973. [Online]. Available: https://api.semanticscholar.org/CorpusID:33586341
  5. A. H. Shoeb and J. Guttag, “Application of Machine Learning To Epileptic Seizure Detection,” in 2010 International Conference on Machine Learning (ICML), Jun. 2010. [Online]. Available: https://www.semanticscholar.org/paper/Application-of-Machine-Learning-To-Epileptic-Shoeb-Guttag/57e4afe9ca74414fa02f2e0a929b64dc9a03334d
  6. A. S. Zandi, M. Javidan, G. A. Dumont, and R. Tafreshi, “Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform,” IEEE transactions on bio-medical engineering, vol. 57, no. 7, pp. 1639–1651, Jul. 2010.
  7. O. M. Doyle, A. Temko, W. Marnane, G. Lightbody, and G. B. Boylan, “Heart rate based automatic seizure detection in the newborn,” Medical Engineering & Physics, vol. 32, no. 8, pp. 829–839, Oct. 2010.
  8. K. Jansen, C. Varon, S. Van Huffel, and L. Lagae, “Peri-ictal ECG changes in childhood epilepsy: implications for detection systems,” Epilepsy & Behavior: E&B, vol. 29, no. 1, pp. 72–76, Oct. 2013.
  9. K. Vandecasteele, T. De Cooman, C. Chatzichristos, E. Cleeren, L. Swinnen, J. Macea Ortiz, S. Van Huffel, M. Dümpelmann, A. Schulze-Bonhage, M. De Vos, W. Van Paesschen, and B. Hunyadi, “The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels,” Epilepsia, vol. 62, no. 10, pp. 2333–2343, Oct. 2021.
  10. K. Vandecasteele, T. De Cooman, Y. Gu, E. Cleeren, K. Claes, W. V. Paesschen, S. V. Huffel, and B. Hunyadi, “Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment,” Sensors (Basel, Switzerland), vol. 17, no. 10, p. 2338, Oct. 2017.
  11. S. Beniczky, I. Conradsen, and P. Wolf, “Detection of convulsive seizures using surface electromyography,” Epilepsia, vol. 59 Suppl 1, pp. 23–29, Jun. 2018.
  12. C. Bagavathi, S. M, S. M. Nair, and S. R, “Novel Epileptic Detection System using Portable EMG-based Assistance,” in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), May 2022, pp. 1762–1765. [Online]. Available: https://ieeexplore.ieee.org/document/9793109
  13. A. Djemal, D. Bouchaala, A. Fakhfakh, and O. Kanoun, “Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study,” Bioengineering, vol. 10, no. 6, p. 703, Jun. 2023, number: 6 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/2306-5354/10/6/703
  14. S. Ganesan, T. A. A. Victoire, and R. Ganesan, “EDA based automatic detection of epileptic seizures using wireless system,” in 2011 International Conference on Electronics, Communication and Computing Technologies, Sep. 2011, pp. 47–52. [Online]. Available: https://ieeexplore.ieee.org/document/6077068
  15. M.-Z. Poh, T. Loddenkemper, C. Reinsberger, N. C. Swenson, S. Goyal, M. C. Sabtala, J. R. Madsen, and R. W. Picard, “Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor,” Epilepsia, vol. 53, no. 5, pp. e93–97, May 2012.
  16. “Embrace2 Seizure Monitoring | Smarter Epilepsy Management | Embrace Watch,” https://www.empatica.com/embrace2/, accessed: 2023-11-02.
  17. Z. Liang and T. Nishimura, “Are wearable EEG devices more accurate than fitness wristbands for home sleep Tracking? Comparison of consumer sleep trackers with clinical devices,” in 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), Oct. 2017, pp. 1–5. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8229188
  18. K. O’Hearn, M. Asato, S. Ordaz, and B. Luna, “Neurodevelopment and executive function in autism,” Development and psychopathology, vol. 20, pp. 1103–32, Feb. 2008.
  19. D. Bathgate, J. S. Snowden, A. Varma, A. Blackshaw, and D. Neary, “Behaviour in frontotemporal dementia, Alzheimer’s disease and vascular dementia,” Acta Neurologica Scandinavica, vol. 103, no. 6, pp. 367–378, 2001, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1034/j.1600-0404.2001.2000236.x. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1034/j.1600-0404.2001.2000236.x
  20. H. Truong, N. Bui, Z. Raghebi, M. Ceko, N. Pham, P. Nguyen, A. Nguyen, T. Kim, K. Siegfried, E. Stene, T. Tvrdy, L. Weinman, T. Payne, D. Burke, T. Dinh, S. D’Mello, F. Banaei-Kashani, T. Wager, P. Goldstein, and T. Vu, “Painometry: Wearable and objective quantification system for acute postoperative pain,” in Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, ser. MobiSys ’20.   New York, NY, USA: Association for Computing Machinery, 2020, p. 419–433. [Online]. Available: https://doi.org/10.1145/3386901.3389022
  21. “Video EEG Test,” https://www.epilepsy.com/diagnosis/eeg/video-eeg, accessed: 2023-11-02.
  22. “Persyst: The worldwide leader in EEG software,” https://www.persyst.com/, accessed: 2023-11-02.
  23. T. L. Babb, E. Mariani, and P. H. Crandall, “An electronic circuit for detection of EEG seizures recorded with implanted electrodes,” Electroencephalography and Clinical Neurophysiology, vol. 37, no. 3, pp. 305–308, Sep. 1974. [Online]. Available: https://www.sciencedirect.com/science/article/pii/0013469474900364
  24. M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagić, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, vol. 38, no. 5, pp. 439–447, Sep. 2021.
  25. A. Shoeibi, M. Khodatars, N. Ghassemi, M. Jafari, P. Moridian, R. Alizadehsani, M. Panahiazar, F. Khozeimeh, A. Zare, H. Hosseini-Nejad, A. Khosravi, A. F. Atiya, D. Aminshahidi, S. Hussain, M. Rouhani, S. Nahavandi, and U. R. Acharya, “Epileptic Seizures Detection Using Deep Learning Techniques: A Review,” International Journal of Environmental Research and Public Health, vol. 18, no. 11, p. 5780, May 2021. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199071/
  26. U. Asif, S. Roy, J. Tang, and S. Harrer, “SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification,” Sep. 2020, arXiv:1903.03232 [cs, q-bio, stat]. [Online]. Available: http://arxiv.org/abs/1903.03232
  27. S. Tang, J. A. Dunnmon, K. Saab, X. Zhang, Q. Huang, F. Dubost, D. L. Rubin, and C. Lee-Messer, “Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis,” Mar. 2022, arXiv:2104.08336 [cs, eess]. [Online]. Available: http://arxiv.org/abs/2104.08336
  28. S. Roy, U. Asif, J. Tang, and S. Harrer, “Seizure Type Classification Using EEG Signals and Machine Learning: Setting a Benchmark,” in 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Dec. 2020, pp. 1–6, iSSN: 2473-716X. [Online]. Available: https://ieeexplore.ieee.org/document/9353642
  29. I. R. D. Saputro, N. D. Maryati, S. R. Solihati, I. Wijayanto, S. Hadiyoso, and R. Patmasari, “Seizure Type Classification on EEG Signal using Support Vector Machine,” Journal of Physics: Conference Series, vol. 1201, no. 1, p. 012065, May 2019, publisher: IOP Publishing. [Online]. Available: https://dx.doi.org/10.1088/1742-6596/1201/1/012065
  30. I. Wijayanto, R. Hartanto, H. A. Nugroho, and B. Winduratna, “Seizure Type Detection in Epileptic EEG Signal using Empirical Mode Decomposition and Support Vector Machine,” in 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA), Aug. 2019, pp. 314–319. [Online]. Available: https://ieeexplore.ieee.org/document/8937205
  31. V. Shah, E. von Weltin, S. Lopez, J. R. McHugh, L. Veloso, M. Golmohammadi, I. Obeid, and J. Picone, “The Temple University Hospital Seizure Detection Corpus,” Frontiers in Neuroinformatics, vol. 12, 2018. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fninf.2018.00083
  32. S. C. Joshi, G. C. Jana, and A. Agrawal, “A Multi-view Representation Learning Approach for Seizure Detection Over Multi-channel EEG Signals,” in Intelligent Data Engineering and Analytics, ser. Smart Innovation, Systems and Technologies, V. Bhateja, X.-S. Yang, J. Chun-Wei Lin, and R. Das, Eds.   Singapore: Springer Nature, 2023, pp. 375–385.
  33. A. H. Shoeb, “Application of machine learning to epileptic seizure onset detection and treatment,” Thesis, Massachusetts Institute of Technology, 2009, accepted: 2010-04-28T17:17:43Z. [Online]. Available: https://dspace.mit.edu/handle/1721.1/54669
  34. S. Madhavan, R. K. Tripathy, and R. B. Pachori, “Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals,” IEEE Sensors Journal, vol. 20, no. 6, pp. 3078–3086, Mar. 2020, conference Name: IEEE Sensors Journal. [Online]. Available: https://ieeexplore.ieee.org/document/8913620
  35. T. Kim, P. Nguyen, N. Pham, N. Bui, H. Truong, S. Ha, and T. Vu, “Epileptic Seizure Detection and Experimental Treatment: A Review,” Frontiers in Neurology, vol. 11, 2020. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fneur.2020.00701
  36. C. A. Szabo’, L. C. Morgan, K. M. Karkar, L. D. Leary, O. V. Lie, M. Girouard, and J. E. Cavazos, “Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings,” Epilepsia, vol. 56, no. 9, pp. 1432–1437, Sep. 2015.
  37. F. Massé, M. V. Bussel, A. Serteyn, J. Arends, and J. Penders, “Miniaturized wireless ECG monitor for real-time detection of epileptic seizures,” ACM Transactions on Embedded Computing Systems, vol. 12, no. 4, pp. 102:1–102:21, Jul. 2013. [Online]. Available: https://dl.acm.org/doi/10.1145/2485984.2485990
  38. J. Jeppesen, A. Fuglsang-Frederiksen, P. Johansen, J. Christensen, S. Wüstenhagen, H. Tankisi, E. Qerama, A. Hess, and S. Beniczky, “Seizure detection based on heart rate variability using a wearable electrocardiography device,” Epilepsia, vol. 60, no. 10, pp. 2105–2113, Oct. 2019.
  39. M. Savadkoohi, T. Oladunni, and L. Thompson, “A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal,” Biocybernetics and Biomedical Engineering, vol. 40, no. 3, pp. 1328–1341, Jul. 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0208521620300851
  40. L. V. Tran, H. M. Tran, T. M. Le, T. T. M. Huynh, H. T. Tran, and S. V. T. Dao, “Application of Machine Learning in Epileptic Seizure Detection,” Diagnostics (Basel, Switzerland), vol. 12, no. 11, p. 2879, Nov. 2022.
  41. D. K. Atal and M. Singh, “Effectual seizure detection using MBBF-GPSO with CNN network,” Cognitive Neurodynamics, Feb. 2023. [Online]. Available: https://doi.org/10.1007/s11571-023-09943-1
  42. J. Askamp and M. J. A. M. van Putten, “Mobile EEG in epilepsy,” International Journal of Psychophysiology, vol. 91, no. 1, pp. 30–35, Jan. 2014. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167876013002523
  43. J. Duun-Henriksen, M. Baud, M. P. Richardson, M. Cook, G. Kouvas, J. M. Heasman, D. Friedman, J. Peltola, I. C. Zibrandtsen, and T. W. Kjaer, “A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings,” Epilepsia, vol. 61, no. 9, pp. 1805–1817, Sep. 2020.
  44. B. G. Do Valle, S. S. Cash, and C. G. Sodini, “Wireless behind-the-ear EEG recording device with wireless interface to a mobile device (iPhone/iPod touch),” Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2014, pp. 5952–5955, 2014.
  45. M. EL Menshawy, A. Benharref, and M. Serhani, “An automatic mobile-health based approach for EEG epileptic seizures detection,” Expert Systems with Applications, vol. 42, no. 20, pp. 7157–7174, Nov. 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417415003103
  46. A. Biondi, V. Santoro, P. F. Viana, P. Laiou, D. K. Pal, E. Bruno, and M. P. Richardson, “Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review,” Epilepsia, vol. 63, no. 5, pp. 1041–1063, 2022, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/epi.17220. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/epi.17220
  47. V. Mihajlović, B. Grundlehner, R. Vullers, and J. Penders, “Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 1, pp. 6–21, Jan. 2015, conference Name: IEEE Journal of Biomedical and Health Informatics. [Online]. Available: https://ieeexplore.ieee.org/document/6824740
  48. “Remi,” https://www.epitel.com/, accessed: 2023-11-02.
  49. “Epihunter,” https://www.epihunter.com/, accessed: 2023-11-02.
  50. “EEG - Electroencephalography - BCI | NeuroSky,” https://neurosky.com/biosensors/eeg-sensor/, accessed: 2023-11-02.
  51. M. A. Frankel, M. J. Lehmkuhle, M. Watson, K. Fetrow, L. Frey, C. Drees, and M. C. Spitz, “Electrographic seizure monitoring with a novel, wireless, single-channel EEG sensor,” Clinical Neurophysiology Practice, vol. 6, pp. 172–178, Jan. 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2467981X2100024X
  52. “Ceribell Rapid Response EEG,” https://ceribell.com/, accessed: 2023-11-02.
  53. S. Beniczky, I. Conradsen, O. Henning, M. Fabricius, and P. Wolf, “Automated real-time detection of tonic-clonic seizures using a wearable EMG device,” Neurology, vol. 90, no. 5, pp. e428–e434, Jan. 2018, publisher: Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology Section: Article. [Online]. Available: https://n.neurology.org/content/90/5/e428
  54. F. S. S. Leijten and Dutch TeleEpilepsy Consortium, “Multimodal seizure detection: A review,” Epilepsia, vol. 59 Suppl 1, pp. 42–47, Jun. 2018.
  55. A. B. Usakli, “Improvement of EEG Signal Acquisition: An Electrical Aspect for State of the Art of Front End,” Computational Intelligence and Neuroscience, vol. 2010, p. e630649, Feb. 2010, publisher: Hindawi. [Online]. Available: https://www.hindawi.com/journals/cin/2010/630649/
  56. P. Kidmose, D. Looney, M. Ungstrup, M. L. Rank, and D. P. Mandic, “A Study of Evoked Potentials From Ear-EEG,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2824–2830, Oct. 2013, conference Name: IEEE Transactions on Biomedical Engineering. [Online]. Available: https://ieeexplore.ieee.org/document/6521411
  57. M. G. Bleichner and S. Debener, “Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG,” Frontiers in Human Neuroscience, vol. 11, 2017. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fnhum.2017.00163
  58. I. C. Zibrandtsen, P. Kidmose, C. B. Christensen, and T. W. Kjaer, “Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy – A comparison with scalp EEG monitoring,” Clinical Neurophysiology, vol. 128, no. 12, pp. 2454–2461, Dec. 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1388245717310763
  59. K. Vandecasteele, T. De Cooman, J. Dan, E. Cleeren, S. Van Huffel, B. Hunyadi, and W. Van Paesschen, “Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels,” Epilepsia, vol. 61, no. 4, pp. 766–775, 2020, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/epi.16470. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/epi.16470
  60. A. Meiser and M. G. Bleichner, “Ear-EEG compares well to cap-EEG in recording auditory ERPs: a quantification of signal loss,” Journal of Neural Engineering, vol. 19, no. 2, p. 026042, Apr. 2022, publisher: IOP Publishing. [Online]. Available: https://dx.doi.org/10.1088/1741-2552/ac5fcb
  61. J.-T. Chien, “Chapter 4 - Independent Component Analysis,” in Source Separation and Machine Learning, J.-T. Chien, Ed.   Academic Press, Jan. 2019, pp. 99–160. [Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780128045664000164
  62. J. Xu, S. Mitra, C. Van Hoof, R. F. Yazicioglu, and K. A. A. Makinwa, “Active Electrodes for Wearable EEG Acquisition: Review and Electronics Design Methodology,” IEEE reviews in biomedical engineering, vol. 10, pp. 187–198, 2017.
  63. J. D. Reiss, “UNDERSTANDING SIGMA–DELTA MODULATION:,” J. Audio Eng. Soc., vol. 56, no. 1, 2008.
  64. K. Dragomiretskiy and D. Zosso, “Variational Mode Decomposition,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531–544, Feb. 2014, conference Name: IEEE Transactions on Signal Processing. [Online]. Available: https://ieeexplore.ieee.org/document/6655981
  65. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, Mar. 1998, publisher: Royal Society. [Online]. Available: https://royalsocietypublishing.org/doi/10.1098/rspa.1998.0193
  66. J. Chen, Y. Huang, and J. Benesty, “Filtering Techniques for Noise Reduction and Speech Enhancement,” in Adaptive Signal Processing: Applications to Real-World Problems, ser. Signals and Communication Technology, J. Benesty and Y. Huang, Eds.   Berlin, Heidelberg: Springer, 2003, pp. 129–154. [Online]. Available: https://doi.org/10.1007/978-3-662-11028-7_5
  67. D. Butusov, T. Karimov, A. Voznesenskiy, D. Kaplun, V. Andreev, and V. Ostrovskii, “Filtering Techniques for Chaotic Signal Processing,” Electronics, vol. 7, no. 12, p. 450, Dec. 2018, number: 12 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/2079-9292/7/12/450
  68. J. V. Stone, “Independent component analysis: an introduction,” Trends in Cognitive Sciences, vol. 6, no. 2, pp. 59–64, Feb. 2002. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1364661300018131
  69. G. Naik and D. Kumar, “An Overview of Independent Component Analysis and Its Applications,” Informatica, vol. 35, pp. 63–81, Jan. 2011.
  70. H. Kasban, H. Arafa, and S. M. S. Elaraby, “Principle component analysis for radiotracer signal separation,” Applied Radiation and Isotopes, vol. 112, pp. 20–26, Jun. 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0969804316300896
  71. M. A. Kass and Y. Li, “Use of principal component analysis in the de-noising and signal- separation of transient electromagnetic data,” 2007. [Online]. Available: https://api.semanticscholar.org/CorpusID:17798116
  72. G. Rilling, P. Flandrin, and P. Gonçalves, “On empirical mode decomposition and its algorithms,” Jun. 2003. [Online]. Available: https://www.semanticscholar.org/paper/On-empirical-mode-decomposition-and-its-algorithms-Rilling-Flandrin/3f616db40f5da4446a039bb6ae5d801d4c616f2b
  73. G. Wang, X.-Y. Chen, F.-L. Qiao, Z. Wu, and N. E. Huang, “On intrinsic mode function,” Advances in Adaptive Data Analysis, vol. 02, no. 03, pp. 277–293, Jul. 2010, publisher: World Scientific Publishing Co. [Online]. Available: https://www.worldscientific.com/doi/abs/10.1142/S1793536910000549
  74. D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788–791, Oct. 1999, number: 6755 Publisher: Nature Publishing Group. [Online]. Available: https://www.nature.com/articles/44565
  75. F. Segovia, J. M. Górriz, J. Ramírez, F. J. Martinez-Murcia, and M. García-Pérez, “Using deep neural networks along with dimensionality reduction techniques to assist the diagnosis of neurodegenerative disorders,” Logic Journal of the IGPL, vol. 26, no. 6, pp. 618–628, Nov. 2018. [Online]. Available: https://doi.org/10.1093/jigpal/jzy026
  76. S. Krause-Solberg, “Non-Negative Dimensionality Reduction in Signal Separation,” doctoralThesis, Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2015, accepted: 2020-10-19T13:15:27Z Journal Abbreviation: Anwendung von nichtnegativer Dimensionsreduktion im Bereich der Signaltrennung. [Online]. Available: https://ediss.sub.uni-hamburg.de/handle/ediss/6859
  77. B. Karan, S. S. Sahu, J. R. Orozco-Arroyave, and K. Mahto, “Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson’s disease prediction,” Computer Speech & Language, vol. 69, p. 101216, Sep. 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0885230821000231
  78. Y. Yi, Y. Shi, H. Zhang, J. Wang, and J. Kong, “Label propagation based semi-supervised non-negative matrix factorization for feature extraction,” Neurocomputing, vol. 149, pp. 1021–1037, Feb. 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231214009680
  79. E. Vincent, R. Gribonval, and C. Fevotte, “Performance measurement in blind audio source separation,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 4, pp. 1462–1469, Jul. 2006, conference Name: IEEE Transactions on Audio, Speech, and Language Processing. [Online]. Available: https://ieeexplore.ieee.org/document/1643671
  80. C. Ye, K. Toyoda, and T. Ohtsuki, “Blind Source Separation on Non-Contact Heartbeat Detection by Non-Negative Matrix Factorization Algorithms,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 2, pp. 482–494, Feb. 2020, conference Name: IEEE Transactions on Biomedical Engineering. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8710248
  81. A. Cichocki, R. Zdunek, and S. Amari, “New Algorithms for Non-Negative Matrix Factorization in Applications to Blind Source Separation,” in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 5, May 2006, pp. V–V, iSSN: 2379-190X. [Online]. Available: https://ieeexplore.ieee.org/document/1661352
  82. B. S. Alexandrov and V. V. Vesselinov, “Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization,” Water Resources Research, vol. 50, no. 9, pp. 7332–7347, 2014, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/2013WR015037. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/2013WR015037
  83. A. Rolet, V. Seguy, M. Blondel, and H. Sawada, “Blind source separation with optimal transport non-negative matrix factorization,” EURASIP Journal on Advances in Signal Processing, vol. 2018, no. 1, p. 53, Sep. 2018. [Online]. Available: https://doi.org/10.1186/s13634-018-0576-2
  84. L. Jing, C. Zhang, and M. K. Ng, “SNMFCA: Supervised NMF-Based Image Classification and Annotation,” IEEE Transactions on Image Processing, vol. 21, no. 11, pp. 4508–4521, Nov. 2012, conference Name: IEEE Transactions on Image Processing. [Online]. Available: https://ieeexplore.ieee.org/document/6226461
  85. C. Févotte, N. Bertin, and J.-L. Durrieu, “Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis,” Neural Computation, vol. 21, no. 3, pp. 793–830, Mar. 2009. [Online]. Available: https://doi.org/10.1162/neco.2008.04-08-771
  86. V. Leplat, N. Gillis, and J. Idier, “Multiplicative Updates for NMF with $\beta$-Divergences under Disjoint Equality Constraints,” SIAM Journal on Matrix Analysis and Applications, vol. 42, no. 2, pp. 730–752, Jan. 2021, publisher: Society for Industrial and Applied Mathematics. [Online]. Available: https://epubs.siam.org/doi/abs/10.1137/20M1377278
  87. C. Févotte and J. Idier, “Algorithms for Nonnegative Matrix Factorization with the Beta-Divergence,” Neural Computation, vol. 23, no. 9, pp. 2421–2456, Sep. 2011. [Online]. Available: https://doi.org/10.1162/NECO_a_00168
  88. N. N. E. Software, “Natus® NeuroWorks® EEG Software,” Nov. 2023. [Online]. Available: https://natus.com/neuro/neuroworks-eeg-software/
  89. L. J. Hirsch, M. W. Fong, M. Leitinger, S. M. LaRoche, S. Beniczky, N. S. Abend, J. W. Lee, C. J. Wusthoff, C. D. Hahn, M. B. Westover, E. E. Gerard, S. T. Herman, H. A. Haider, G. Osman, A. Rodriguez-Ruiz, C. B. Maciel, E. J. Gilmore, A. Fernandez, E. S. Rosenthal, J. Claassen, A. M. Husain, J. Y. Yoo, E. L. So, P. W. Kaplan, M. R. Nuwer, M. van Putten, R. Sutter, F. W. Drislane, E. Trinka, and N. Gaspard, “American Clinical Neurophysiology Society’s Standardized Critical Care EEG Terminology: 2021 Version,” Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society, vol. 38, no. 1, pp. 1–29, Jan. 2021. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8135051/
  90. X. Zhao, N. Yoshida, T. Ueda, H. Sugano, and T. Tanaka, “Epileptic seizure detection by using interpretable machine learning models,” Journal of Neural Engineering, vol. 20, no. 1, p. 015002, Feb. 2023, publisher: IOP Publishing. [Online]. Available: https://dx.doi.org/10.1088/1741-2552/acb089
  91. Z. Sawadogo, G. Mendy, J. M. Dembele, and S. Ouya, “Android malware detection: Investigating the impact of imbalanced data-sets on the performance of machine learning models,” in 2022 24th International Conference on Advanced Communication Technology (ICACT), Feb. 2022, pp. 435–441, iSSN: 1738-9445. [Online]. Available: https://ieeexplore.ieee.org/document/9728833
  92. O. Medical, “Visensia,” 2023. [Online]. Available: https://www.obsmedical.com/visensia-the-safety-index/
  93. B. Company Inc., “BrainScope,” Nov. 2023. [Online]. Available: https://www.brainscope.com

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