ADVANCES IN EEG SIGNAL PROCESSING AND MACHINE LEARNING FOR EPILEPTIC SEIZURE DETECTION AND PREDICTION
Main Article Content
Keywords
EEG, Seizures, Epilepsy, Diagnosis
Abstract
About 50 million people worldwide suffer with epilepsy, a complicated neurological condition marked by frequent, unexpected seizures that can seriously lower quality of life. Effective therapy, lowering the risk of harm, and enhancing overall patient outcomes depend on the prompt diagnosis and precise prediction of these seizures. Electroencephalography (EEG) is a crucial diagnostic technology that records brain electrical activity and helps medical professionals to recognize aberrant patterns linked to seizures.
More accurate seizure identification has been made possible by recent developments in EEG signal processing techniques, such as increased time-frequency analysis, improved artifact removal, and feature extraction approaches. By enabling the automated interpretation of complicated EEG data, machine learning methods like support vector machines, convolutional neural networks, and recurrent neural networks have further changed the landscape of seizure prediction.
The state-of-the-art approaches for processing EEG signals and combining them with machine learning for seizure prediction and detection are reviewed in this work. We address the clinical usefulness of these technologies, highlight important findings from recent research, and examine the advantages and disadvantages of various strategies. Furthermore, we stress that creating reliable, real-time systems that can be easily incorporated into clinical practice requires interdisciplinary cooperation. Lastly, we suggest future lines of inquiry, such as the necessity for extensive and varied datasets, the interpretability of machine learning models, and the hunt for new biomarkers for the prediction of seizures.
More accurate seizure identification has been made possible by recent developments in EEG signal processing techniques, such as increased time-frequency analysis, improved artifact removal, and feature extraction approaches. By enabling the automated interpretation of complicated EEG data, machine learning methods like support vector machines, convolutional neural networks, and recurrent neural networks have further changed the landscape of seizure prediction.
The state-of-the-art approaches for processing EEG signals and combining them with machine learning for seizure prediction and detection are reviewed in this work. We address the clinical usefulness of these technologies, highlight important findings from recent research, and examine the advantages and disadvantages of various strategies. Furthermore, we stress that creating reliable, real-time systems that can be easily incorporated into clinical practice requires interdisciplinary cooperation. Lastly, we suggest future lines of inquiry, such as the necessity for extensive and varied datasets, the interpretability of machine learning models, and the hunt for new biomarkers for the prediction of seizures.
References
1. Fisher RS, Cross JH, French JA, Higurashi N, Hirsch E, Jansen FE, Lagae L, Moshé SL, Peltola J, Roulet Perez E, Scheffer IE, Zuberi SM. Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017 Apr;58(4):522-530.
2. Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of machine learning research. 2003;3(Mar):1157-82..
3. Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. The Lancet Neurology. 2018 Mar 1;17(3):279-88.
4. Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kwan P, Kuhlmann L, O’Brien T, Razi A. Machine learning for predicting epileptic seizures using EEG signals: A review. IEEE reviews in biomedical engineering. 2020 Jul 13;14:139-55.
5. Vidyaratne LS, Iftekharuddin KM. Real-time epileptic seizure detection using EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017 Apr 25;25(11):2146-56.
6. Hosseini MP, Hosseini A, Ahi K. A review on machine learning for EEG signal processing in bioengineering. IEEE reviews in biomedical engineering. 2020 Jan 28;14:204-18.
7. Nafea MS, Ismail ZH. Supervised machine learning and deep learning techniques for epileptic seizure recognition using EEG signals—A systematic literature review. Bioengineering. 2022 Dec 8;9(12):781.
8. Thammasan, N., et al. (2018). "Machine learning for epilepsy: A systematic review." Seizure, 62, 89-99.
9. WHO. (2021). Epilepsy. World Health Organization.
10. Zhang, Y., et al. (2018). "An efficient method for EEG signal classification using convolutional neural networks." Biomedical Signal Processing and Control, 44, 56-64.
11. He, S., et al. (2019). Personalized seizure prediction using deep learning. Journal of Neural Engineering, 16(5), 056008. doi:10.1088/1741-2552/ab32c6.
12. Lipton, Z. C. (2016). The Mythos of Model Interpretability. Communications of the ACM, 61(10), 36-43. doi:10.1145/3236386.
13. Eldridge, P., et al. (2020). Data sharing in epilepsy research: A review of current practices and future directions. Epilepsia Open, 5(3), 564-573. doi:10.1002/epi4.12445.
14. Bishop, C. M., et al. (2020). The importance of interdisciplinary collaboration in the development of artificial intelligence for healthcare. Health Informatics Journal, 26(4), 2453-2461. doi:10.1177/1460458218824365.
2. Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of machine learning research. 2003;3(Mar):1157-82..
3. Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. The Lancet Neurology. 2018 Mar 1;17(3):279-88.
4. Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kwan P, Kuhlmann L, O’Brien T, Razi A. Machine learning for predicting epileptic seizures using EEG signals: A review. IEEE reviews in biomedical engineering. 2020 Jul 13;14:139-55.
5. Vidyaratne LS, Iftekharuddin KM. Real-time epileptic seizure detection using EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017 Apr 25;25(11):2146-56.
6. Hosseini MP, Hosseini A, Ahi K. A review on machine learning for EEG signal processing in bioengineering. IEEE reviews in biomedical engineering. 2020 Jan 28;14:204-18.
7. Nafea MS, Ismail ZH. Supervised machine learning and deep learning techniques for epileptic seizure recognition using EEG signals—A systematic literature review. Bioengineering. 2022 Dec 8;9(12):781.
8. Thammasan, N., et al. (2018). "Machine learning for epilepsy: A systematic review." Seizure, 62, 89-99.
9. WHO. (2021). Epilepsy. World Health Organization.
10. Zhang, Y., et al. (2018). "An efficient method for EEG signal classification using convolutional neural networks." Biomedical Signal Processing and Control, 44, 56-64.
11. He, S., et al. (2019). Personalized seizure prediction using deep learning. Journal of Neural Engineering, 16(5), 056008. doi:10.1088/1741-2552/ab32c6.
12. Lipton, Z. C. (2016). The Mythos of Model Interpretability. Communications of the ACM, 61(10), 36-43. doi:10.1145/3236386.
13. Eldridge, P., et al. (2020). Data sharing in epilepsy research: A review of current practices and future directions. Epilepsia Open, 5(3), 564-573. doi:10.1002/epi4.12445.
14. Bishop, C. M., et al. (2020). The importance of interdisciplinary collaboration in the development of artificial intelligence for healthcare. Health Informatics Journal, 26(4), 2453-2461. doi:10.1177/1460458218824365.
Article Sidebar
Published
Mar 17, 2025
Article Details
How to Cite
Dr. Divya Jaiswal, Dr. Raveena Singh, & Dr. Beenu Prajapati. (2025). ADVANCES IN EEG SIGNAL PROCESSING AND MACHINE LEARNING FOR EPILEPTIC SEIZURE DETECTION AND PREDICTION. Journal of Population Therapeutics and Clinical Pharmacology, 32(2), 355-364. https://doi.org/10.53555/drbcyr98
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Author Biographies
Dr. Divya Jaiswal
Assistant Professor, Department of Physiology, A.S.J.S.A.T.D.S Medical College, Fatehpur
Dr. Raveena Singh
Assistant Professor, Department of Anatomy, A.S.J.S.A.T.D.S Medical College, Fatehpur
Dr. Beenu Prajapati
Assistant Professor, Department of Physiology, Shri Gorkhnath Medical College Hospital & Research Centre, Gorakhpur
How to Cite
Dr. Divya Jaiswal, Dr. Raveena Singh, & Dr. Beenu Prajapati. (2025). ADVANCES IN EEG SIGNAL PROCESSING AND MACHINE LEARNING FOR EPILEPTIC SEIZURE DETECTION AND PREDICTION. Journal of Population Therapeutics and Clinical Pharmacology, 32(2), 355-364. https://doi.org/10.53555/drbcyr98