PREDICTING STATES OF EPILEPSY PATIENTS USING DEEP LEARNING MODELS
Boutkhil SIDAOUI
b.sidaoui@cuniv-naama.dzUniversity Center SALHI Ahmed, Computer Science Department (Algeria)
https://orcid.org/0000-0001-7276-2897
Abstract
In this study, the authors present and scrutinize two deep learning models designed for predicting the states of epilepsy patients by utilizing extracted data from their brain's electrical activities recorded in electroencephalography (EEG) signals. The proposed models leverage deep learning networks, with the first being a recurrent neural network known as Long Short-Term Memory (LSTM), and the second a non-recurrent network in the form of a Deep Feedforward Network (DFN) architecture. To construct and execute the DFN and LSTM architectures, the authors rely on 22 characteristics extracted from diverse EEG signals, forming a comprehensive dataset from five patients. The primary goal is to forecast impending epilepsy seizures and categorize three distinct states of brain activity in epilepsy patients. The models put forward yield promising results, particularly in terms of classification rates, across various preceding seizure timeframes ranging from 5 to 50 minutes.
Keywords:
Epilepsy Seizure, EEG, prediction, Deep learning, LSTMReferences
Awad, M., & Khanna, R. (2015). Efficient Learning Machines: Theories, concepts, and applications for engineers and system designers. Apress Berkeley.
DOI: https://doi.org/10.1007/978-1-4302-5990-9
Google Scholar
Behbahani, S., Jafarnia Dabanloo, N., Motie Nasrabadi, A., Teixeira, C. A., & Dourado, A. (2014). A new algorithm for detection of epileptic seizures based on HRV signal. Journal of Experimental & Theoretical Artificial Intelligence, 26(2), 251-265. https://doi.org/10.1080/0952813X.2013.861874
DOI: https://doi.org/10.1080/0952813X.2013.861874
Google Scholar
Boualoulou, N., Belhoussine Drissi, T., & Nsiri, M. (2023). CNN and LSTM for the classification of Parkinson's disease based on the GTCC and MFCC. Applied Computer Science, 19(2), 1-24. https://doi.org/10.35784/acs-2023-11
DOI: https://doi.org/10.35784/acs-2023-11
Google Scholar
Hu, Z., Tang, S., Luo, Y., Jian, F., & Si, X. (2021). 3DACRNN model based on residual network for speech emotion classification. Engineering Letters, 29(2), 400-407.
Google Scholar
Kim, T., Nguyen, P., Pham, N., Bui, N., Truong, H., Ha, S., & Vu, T. (2020). Epileptic seizure detection and experimental treatment: A review. Frontiers in Neurology, 11(701), 080510. https://doi.org/10.3389%2Ffneur.2020.00701
DOI: https://doi.org/10.3389/fneur.2020.00701
Google Scholar
Klatt, J., Feldwisch-Drentrup, H., Ihle, M., Navarro, V., Neufang, M., Teixeira, C., Adam, C., Valderrama, M., Alvarado-Rojas, C., Witon, A., Le Van Quyen, M., Sales, F., Dourado, A., Timmer, J., Schulze-Bonhage, A., & Schelter, B. (2012). The EPILEPSIAE database: An extensive electroencephalography database of epilepsy patients. Epilepsia, 53(9), 1669–1676. https://doi.org/10.1111/j.1528-1167.2012.03564.x
DOI: https://doi.org/10.1111/j.1528-1167.2012.03564.x
Google Scholar
Krukow, P., Jonak, K., Karpiński, R., & Karakuła-Juchnowicz, H. (2019) Abnormalities in hubs location and nodes centrality predict cognitive slowing and increased performance variability in first-episode schizophrenia patients. scientific reports, 9, 9594. https://doi.org/10.1038/s41598-019-46111-0
DOI: https://doi.org/10.1038/s41598-019-46111-0
Google Scholar
Kumar, V. B., Bharath, V., Kumar, K., Vijayalakshmi, M. I., & Padmavathamma (2019). A hybrid data mining approach for diabetes prediction and classification. Proceedings of The World Congress on Engineering and Computer Science (WCECS) (pp. 298-303).
Google Scholar
Li, Y., Yu, Z., Chen, Y., Yang, C., Li, Y., Li, A. X., & Li, B. (2020). Automatic seizure detection using fully convolutional nested LSTM. International Journal of Neural Systems, 30(4), 2050019. https://doi.org/10.1142/S0129065720500197
DOI: https://doi.org/10.1142/S0129065720500197
Google Scholar
Martinez-del-Rincon, J., Santofimia, M. J., del Toro, X., Barba, J., Romero, F., Navas, P., & Lopez, J. C. (2017). Non-linear classifiers applied to EEG analysis for epilepsy seizure detection. Expert Systems with Applications, 86, 99-112. https://doi.org/10.1016/j.eswa.2017.05.052
DOI: https://doi.org/10.1016/j.eswa.2017.05.052
Google Scholar
Nielsen, M. A. (2015). Neural Networks and Deep Learning. Determination Press.
Google Scholar
Ramantani, G., Maillard, L., & Koessler, L., (2016). Correlation of invasive EEG and scalp EEG. Seizure Journal, 41, 196-200. https://doi.org/10.1016/j.seizure.2016.05.018
DOI: https://doi.org/10.1016/j.seizure.2016.05.018
Google Scholar
Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N. E., Fernández, I. S., Klehm, J., Bosl, W., Reinsberger, C., Schachter, S., & Loddenkemper, T. (2014). Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy & Behavior, 37, 291-307. https://doi.org/10.1016/j.yebeh.2014.06.023
DOI: https://doi.org/10.1016/j.yebeh.2014.06.023
Google Scholar
Teixeira, C. A., Direito, B., Feldwisch-Drentrup, H., Valderrama, M., Costa, R. P., Alvarado-Rojas, C., Nikolopoulos, S., Le Van Quyen, M., Timmer, J., Schelter, B., & Dourado, A. (2011). EPILAB: A software package for studies on the prediction of epileptic seizures. Journal of Neuroscience Methods, 200(2), 257-271. https://doi.org/10.1016/j.jneumeth.2011.07.002
DOI: https://doi.org/10.1016/j.jneumeth.2011.07.002
Google Scholar
Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2007). Automatic seizure detection based on time-frequency analysis and artificial neural networks. Computational Intelligence and Neuroscience, 2007(1), 080510. https://doi.org/10.1155/2007/80510
DOI: https://doi.org/10.1155/2007/80510
Google Scholar
Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2009). Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Transactions on Information Technology in Biomedicine, 13(5), 703-710. https://doi.org/10.1109/TITB.2009.2017939
DOI: https://doi.org/10.1109/TITB.2009.2017939
Google Scholar
Vani, S., Suresh, G. R., Balakumaran, T., & Cross, T. A. (2019). EEG signal analysis for automated epilepsy seizure detection using wavelet transform and Artificial Neural Network. Journal of Medical Imaging and Health Informatics, 9(6), 1301-1306. https://doi.org/10.1166/jmihi.2019.2713
DOI: https://doi.org/10.1166/jmihi.2019.2713
Google Scholar
Webb, A. R., & Copsey, K. D. (2002). Statistical Pattern Recognition. John Wiley & Sons Ltd.
DOI: https://doi.org/10.1002/0470854774
Google Scholar
Willems, L. M., Reif, P. S., Spyrantis, A., Cattani, A., Freiman, T. M., Seifert, V., Wagne, M., You, S.-J., Schubert-Bast, S., Bauer, S., Klein, K. M., Rosenow, F., & Strzelczyk, A. (2019). Invasive EEG-electrodes in presurgical evaluation of epilepsies: Systematic analysis of implantation-, video-EEG-monitoring- and explantation-related complications, and review of literature. Epilepsy & Behavior, 91, 30-37. https://doi.org/10.1016/j.yebeh.2018.05.012
DOI: https://doi.org/10.1016/j.yebeh.2018.05.012
Google Scholar
Yindeedej, V., Uda, T., Tanoue, Y., Kojima, Y., Kawashima, T., Koh, S., Uda, H., Nishiyama, T., Takagawa, M., Shuto, F., Goto, T., (2024). A scoping review of seizure onset pattern in SEEG and a proposal for morphological classification. Journal of Clinical Neuroscience, 123, 84-90. https://doi.org/10.1016/j.jocn.2024.03.024
DOI: https://doi.org/10.1016/j.jocn.2024.03.024
Google Scholar
Yoki Donzia, S. K., & Kon Kim, H. (2019). Recurrent Neural Network with sequence to sequence model to translate language based on TensorFlow. Proceedings of the World Congress on Engineering and Computer Science 2019 (WCECS 2019) (pp. 401-405).
Google Scholar
Yuan, H., Li, Y., Yang, J., Li, H., Yang, Q., Guo, C., Zhu, S., & Shu, X. (2021). State of the art of non-invasive electrode materials for brain-computer interface. Micromachines, 12(12), 1521. https://doi.org/10.3390/mi12121521
DOI: https://doi.org/10.3390/mi12121521
Google Scholar
Authors
Boutkhil SIDAOUIb.sidaoui@cuniv-naama.dz
University Center SALHI Ahmed, Computer Science Department Algeria
https://orcid.org/0000-0001-7276-2897
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