Epileptic Seizures Detection Using Machine Learning: A Review

Abstract

These episodes may be attributed to abnormal chemical alterations in the brain or anomalies in the cerebral region. These illnesses are classified as chronic and increase the likelihood of death due to their abrupt onset and absence of preceding symptoms. Seizures can be identified by analyzing the brain signals produced by neuronal cells. Traditionally, electroencephalogram (EEG) recordings a multi-channel depiction of brain neuronal activity is used for brain signal monitoring. This paper examines important advancements made in the analysis of EEG waveforms to create predictive algorithms for epilepsy.

Country : Iraq

1 Abdulrahman Talal Ibrahim2 Amera Istiqlal Badran

  1. Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq
  2. Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq

IRJIET, Volume 8, Issue 7, July 2024 pp. 167-171

doi.org/10.47001/IRJIET/2024.807018

References

  1. M. S. Nafea and Z. H. Ismail, “Supervised machine learning and deep learning techniques for epileptic seizure recognition using EEG signals—A systematic literature review,” Bioengineering, vol. 9, no. 12, p. 781, 2022.
  2. C. Daniela Rodrigues Cunha and L. D’avila Rocha Batista, “EPILEPSY: IS IT POSSIBLE TO HAVE QUALITY OF LIFE WITH EPILEPSY?,” hs, vol. 3, no. 03, pp. 181–215, Jul. 2023, doi: 10.51249/hs.v3i03.1409.
  3. L. Jehi et al., “Timing of referral to evaluate for epilepsy surgery: Expert Consensus Recommendations from the Surgical Therapies Commission of the International League Against Epilepsy,” Epilepsia, vol. 63, no. 10, pp. 2491–2506, Oct. 2022, doi: 10.1111/epi.17350.
  4. J. J. Majersik et al., “A shortage of neurologists–we must act now: a report from the AAN 2019 Transforming Leaders Program,” Neurology, vol. 96, no. 24, pp. 1122–1134, 2021.
  5. K. W. Beach, “Vascular Diagnosis by Analysis of Waveforms,” Journal for Vascular Ultrasound, vol. 35, no. 4, pp. 192–200, Dec. 2011, doi: 10.1177/154431671103500402.
  6. K. Rasheed et al., “Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 139–155, 2021, doi: 10.1109/RBME.2020.3008792.
  7. R. Srinath and R. Gayathri, “Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods,” International Journal of Imaging Systems and Technology, vol. 31, no. 2, pp. 729–740, 2021, doi: 10.1002/ima.22486.
  8. M. K. Siddiqui, R. Morales-Menendez, X. Huang, and N. Hussain, “A review of epileptic seizure detection using machine learning classifiers,” Brain Inf., vol. 7, no. 1, p. 5, Dec. 2020, doi: 10.1186/s40708-020-00105-1.
  9. R. S. Fisher, “The New Classification of Seizures by the International League Against Epilepsy 2017,” Curr Neurol Neurosci Rep, vol. 17, no. 6, p. 48, Jun. 2017, doi: 10.1007/s11910-017-0758-6.
  10. C. Mahjoub, R. Le Bouquin Jeannès, T. Lajnef, and A. Kachouri, “Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods,” Biomedical Engineering / Biomedizinische Technik, vol. 65, no. 1, pp. 33–50, Jan. 2020, doi: 10.1515/bmt-2019-0001.
  11. A.K. Jaiswal and H. Banka, “Epileptic seizure detection in EEG signal using machine learning techniques,” Australas Phys Eng Sci Med, vol. 41, no. 1, pp. 81–94, Mar. 2018, doi: 10.1007/s13246-017-0610-y.
  12. L. V. Tran, H. M. Tran, T. M. Le, T. T. Huynh, H. T. Tran, and S. V. Dao, “Application of Machine Learning in Epileptic Seizure Detection,” Diagnostics, vol. 12, no. 11, p. 2879, 2022.
  13. J. R. Martin and S. L. Swapna, “A Machine Learning Framework for Epileptic Seizure Detection by Analyzing EEG Signals,” International Journal of Computing and Digital Systems, pp. 1383–1391, 2021.
  14. H. Liu, L. Xi, Y. Zhao, and Z. Li, “Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data.” arXiv, Oct. 06, 2019. Accessed: Jan. 16, 2024. [Online]. Available: http://arxiv.org/abs/1910.02544
  15. I.Ahmad, X. Wang, D. Javeed, P. Kumar, O. W. Samuel, and S. Chen, “A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG signals,” IEEE Journal of Biomedical and Health Informatics, 2023, Accessed: Jan. 16, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10098564/
  16. M. Masum, H. Shahriar, and H. M. Haddad, “Epileptic seizure detection for imbalanced datasets using an integrated machine learning approach,” in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2020, pp. 5416–5419. Accessed: Jan. 16, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9175632/
  17. P. S. Purnima, M. Suresh, and S. Kattepura, “Machine Learning Models for Epileptic Seizure Prediction,” in 2023 International Conference on Inventive Computation Technologies (ICICT), IEEE, 2023, pp. 135–141. Accessed: Jan. 16, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10134350/
  18. C. Daftari, J. Shah, and M. Shah, “Detection of epileptic seizure disorder using EEG signals,” in Artificial intelligence-based brain-computer interface, Elsevier, 2022, pp. 163–188. Accessed: Jan. 16, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780323911979000060
  19. N. Moghim, “Exploring machine learning techniques in epileptic seizure detection and prediction,” PhD Thesis, Heriot-Watt University, 2014. Accessed: Jan. 16, 2024. [Online]. Available: https://core.ac.uk/download/pdf/77035755.pdf
  20. J. Liu, Y. Du, X. Wang, W. Yue, and J. Feng, “Automated Machine Learning for Epileptic Seizure Detection Based on EEG Signals.,” Computers, Materials & Continua, vol. 73, no. 1, 2022, Accessed: Jan. 16, 2024. [Online]. Available: https://cdn.techscience.cn/ueditor/files/cmc/TSP_CMC-73-1/TSP_CMC_29073/TSP_CMC_29073.pdf
  21. M. Tripathi and M. M. Mehendiratta, “Role of EEG in Epilepsy,” in Epilepsy Topics, M. D. Holmes, Ed., InTech, 2014. doi: 10.5772/57430.
  22. K. M. Alalayah, E. M. Senan, H. F. Atlam, I. A. Ahmed, and H. S. A. Shatnawi, “Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means,” Diagnostics, vol. 13, no. 11, p. 1957, Jun. 2023, doi: 10.3390/diagnostics13111957.
  23. A.Kumar, P. Singh, R. Khawas, P. D. Moyya, and M. Asaithambi, “Automated EEG Analysis for Early Diagnosis of Epilepsy: A Comparative Study to Determine Relative Accuracy of Arithmetic and Huffman Coding Algorithms,” in 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Mar. 2021, pp. 1–6. doi: 10.1109/ICBSII51839.2021.9445169.
  24. S. Goel, R. Agrawal, and R. K. Bharti, “Automated Epilepsy Detection using Machine Learning Classifiers based on Entropy Features,” in 2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), Apr. 2023, pp. 757–761. doi: 10.1109/CICTN57981.2023.10140301.
  25. A.Anandaraj and A. Pja, “Enhancing Epileptic Seizure Prediction with Machine Learning and EEG Analysis,” Journal of Machine and Computing, pp. 184–195, Jul. 2023, doi: 10.53759/7669/jmc202303017.
  26. G. Chekhmane and R. Benali, “EEG signals analysis using SVM and MLPNN classifiers for epilepsy detection,” in 2022 5th International Symposium on Informatics and its Applications (ISIA), Nov. 2022, pp. 1–6. doi: 10.1109/ISIA55826.2022.9993577.
  27. J. Birjandtalab, V. N. Jarmale, M. Nourani, and J. Harvey, “Imbalance learning using neural networks for seizure detection,” in 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE, 2018, pp. 1–4. Accessed: Jan. 16, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8584683/
  28. K. Mahantesh and R. Chetana, “Detection of Epileptic Seizures in EEG—Inspired by Machine Learning Techniques,” in Intelligent Computing and Communication, vol. 1034, V. Bhateja, S. C. Satapathy, Y.-D. Zhang, and V. N. M. Aradhya, Eds., in Advances in Intelligent Systems and Computing, vol. 1034. , Singapore: Springer Singapore, 2020, pp. 443–450. doi: 10.1007/978-981-15-1084-7_42.