SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction (2401.06644v1)
Abstract: In this paper, we introduce SeizNet, a closed-loop system for predicting epileptic seizures through the use of Deep Learning (DL) method and implantable sensor networks. While pharmacological treatment is effective for some epilepsy patients (with ~65M people affected worldwide), one out of three suffer from drug-resistant epilepsy. To alleviate the impact of seizure, predictive systems have been developed that can notify such patients of an impending seizure, allowing them to take precautionary measures. SeizNet leverages DL techniques and combines data from multiple recordings, specifically intracranial electroencephalogram (iEEG) and electrocardiogram (ECG) sensors, that can significantly improve the specificity of seizure prediction while preserving very high levels of sensitivity. SeizNet DL algorithms are designed for efficient real-time execution at the edge, minimizing data privacy concerns, data transmission overhead, and power inefficiencies associated with cloud-based solutions. Our results indicate that SeizNet outperforms traditional single-modality and non-personalized prediction systems in all metrics, achieving up to 99% accuracy in predicting seizure, offering a promising new avenue in refractory epilepsy treatment.
- W. H. Organization, “Epilepsy,” Feb 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/epilepsy
- W. Xue-Ping, W. Hai-Jiao, Z. Li-Na, D. Xu, and L. Ling, “Risk factors for drug-resistant epilepsy: A systematic review and meta-alysis,” Medicine, vol. 98, no. 30, 2019.
- F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lehnertz, “Seizure prediction: the long and winding road,” Brain, vol. 130, no. 2, pp. 314–333, 2007.
- S. Ramgopal, S. Thome-Souza, M. Jackson, N. E. Kadish, I. S. Fernández, J. Klehm, W. Bosl, C. Reinsberger, S. Schachter, and T. Loddenkemper, “Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy,” Epilepsy & behavior, vol. 37, pp. 291–307, 2014.
- D. Uvaydov, R. Guida, P. Johari, F. Restuccia, and T. Melodia, “Aieeg: Personalized seizure prediction through partially-reconfigurable deep neural networks,” in IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 2022, pp. 77–88.
- L. Kuhlmann, K. Lehnertz, M. P. Richardson, B. Schelter, and H. P. Zaveri, “Seizure prediction—ready for a new era,” Nature Reviews Neurology, vol. 14, no. 10, pp. 618–630, 2018.
- Y. Park, L. Luo, K. K. Parhi, and T. Netoff, “Seizure prediction with spectral power of eeg using cost-sensitive support vector machines,” Epilepsia, vol. 52, no. 10, pp. 1761–1770, 2011.
- L. Billeci, D. Marino, L. Insana, G. Vatti, and M. Varanini, “Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis,” PloS one, vol. 13, no. 9, p. e0204339, 2018.
- A. Schulze-Bonhage, “Seizure prediction: Time for new, multimodal and ultra-long-term approaches,” pp. 152–153, 2022.
- S. Giuseppe Enrico, D. Neil, and M. Tommaso, “Design and performance evaluation of an implantable ultrasonic networking platform for the internet of medical things,” IEEE/ACM Transactions on Networking, vol. 28, pp. 29–42, 2020.
- M. J. Morrell, “Responsive cortical stimulation for the treatment of medically intractable partial epilepsy,” Neurology, vol. 77, no. 13, pp. 1295–1304, 2011.
- G. E. Santagati and T. Melodia, “An Implantable Low-Power Ultrasonic Platform for the Internet of Medical Things,” in Proc. of IEEE Conference on Computer Communications (INFOCOM), Atlanta, USA, May 2017.
- G. E. Santagati, T. Melodia, L. Galluccio, and S. Palazzo, “Medium access control and rate adaptation for ultrasonic intrabody sensor networks,” IEEE/ACM Transactions on Networking, vol. 23, no. 4, pp. 1121–1134, 2014.
- M. Ihle, H. Feldwisch-Drentrup, C. A. Teixeira, A. Witon, B. Schelter, J. Timmer, and A. Schulze-Bonhage, “Epilepsiae–a european epilepsy database,” Computer methods and programs in biomedicine, vol. 106, no. 3, pp. 127–138, 2012.
- T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2980–2988.
- K. M. Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals,” Computers in biology and medicine, vol. 99, pp. 24–37, 2018.
- Ali Saeizadeh (4 papers)
- Douglas Schonholtz (5 papers)
- Daniel Uvaydov (6 papers)
- Raffaele Guida (2 papers)
- Emrecan Demirors (8 papers)
- Pedram Johari (18 papers)
- Jorge M. Jimenez (2 papers)
- Joseph S. Neimat (2 papers)
- Tommaso Melodia (112 papers)