Cover
Vol. 20 No. 2 (2024)

Published: December 31, 2024

Pages: 115-126

Review Article

Epileptic detection based on deep learning: A review

Ola M. Assim 1 Ahlam F. Mahmood
1Mosul University, Computer Engineering Department, Mosul, Iraq
History
  • Received: September 12, 2023
  • Revised: October 9, 2023
  • Accepted: October 20, 2023
  • Available Online: June 19, 2024
DOI

https://doi.org/10.37917/ijeee.20.2.10

Abstract

Epilepsy, a neurological disorder characterized by recurring seizures, necessitates early and precise detection for effective management. Deep learning techniques have emerged as powerful tools for analyzing complex medical data, specifically electroencephalogram (EEG) signals, advancing epileptic detection. This review comprehensively presents cutting-edge methodologies in deep learning-based epileptic detection systems. Beginning with an overview of epilepsy’s fundamental concepts and their implications for individuals and healthcare are present. This review then delves into deep learning principles and their application in processing EEG signals. Diverse research papers to know the architectures—convolutional neural networks, recurrent neural networks, and hybrid models—are investigated, emphasizing their strengths and limitations in detecting epilepsy. Preprocessing techniques for improving EEG data quality and reliability, such as noise reduction, artifact removal, and feature extraction, are discussed. Present performance evaluation metrics in epileptic detection, such as accuracy, sensitivity, specificity, and area under the curve, are provided. This review anticipates future directions by highlighting challenges such as dataset size and diversity, model interpretability, and integration with clinical decision support systems. Finally, this review demonstrates how deep learning can improve the precision, efficiency, and accessibility of early epileptic diagnosis. This advancement allows for more timely interventions and personalized treatment plans, potentially revolutionizing epilepsy management.

Keywords

References

  1. J. Falco-Walter, “Epilepsy—definition, classification, pathophysiology, and epidemiology,” in Seminars in neurology, vol. 40, pp. 617–623, Thieme Medical Pub- lishers, Inc. 333 Seventh Avenue, 18th Floor, New York, NY ..., 2020.
  2. T. Vos, C. Allen, M. Arora, and R. Barber, “A systematic analysis for the global burden of disease study 2016,” Lancet, vol. 390, no. 2017, pp. 1151–1210, 2017.
  3. E. CERULLI IRELLI et al., “Phenotypic spectrum and prognostic factors in epilepsy with eyelid myoclonia,” Rev Neurol (Paris), vol. 179, 2023.
  4. S. Makkawi, F. S. Alshehri, A. A. Malaikah, A. M. Al- ghamdi, R. M. Al-Zahrani, R. J. Nahas, M. A. Khan, A. Y. Hakami, D. A. Babaer, R. M. Al-zahrani, et al., “Prevalence of etiological factors in adult patients with epilepsy in a tertiary care hospital in the western region of saudi arabia: A cross-sectional study,” Cureus, vol. 15, no. 1, 2023.
  5. M. A. de Bruijn, A. Van Sonderen, M. H. van Coevorden- Hameete, A. E. Bastiaansen, M. W. Schreurs, R. P. 124 | Assim & Mahmood Rouhl, C. A. van Donselaar, M. H. Majoie, R. F. Neuteboom, P. A. Sillevis Smitt, et al., “Evalua- tion of seizure treatment in anti-lgi1, anti-nmdar, and anti-gababr encephalitis,” Neurology, vol. 92, no. 19, pp. e2185–e2196, 2019.
  6. S. S. Abdulwahab, H. K. Khleaf, M. H. Jassim, and S. Abdulwahab, “A systematic review of brain-computer interface based eeg,” Iraqi J. Electr. Electron. Eng, vol. 16, no. 2, pp. 1–10, 2020.
  7. A. Shoeibi, M. Khodatars, N. Ghassemi, M. Ja- fari, P. Moridian, R. Alizadehsani, M. Panahiazar, F. Khozeimeh, A. Zare, H. Hosseini-Nejad, et al., “Epileptic seizures detection using deep learning tech- niques: A review,” International Journal of Environmen- tal Research and Public Health, vol. 18, no. 11, p. 5780, 2021.
  8. A. Omidvarnia, M. A. Kowalczyk, M. Pedersen, and G. D. Jackson, “Towards fast and reliable simultane- ous eeg-fmri analysis of epilepsy with automatic spike detection,” Clinical Neurophysiology, vol. 130, no. 3, pp. 368–378, 2019.
  9. I. S. Al-Furati and A. I. AL-Mayoof, “Design and im- plementation of an injury detection system for corona tracker,” Iraqi Journal for Electrical And Electronic En- gineering, vol. 18, no. 2, 2022.
  10. P. Thangavel, J. Thomas, N. Sinha, W. Y. Peh, R. Yu- varaj, S. S. Cash, R. Chaudhari, S. Karia, J. Jing, R. Rathakrishnan, et al., “Improving automated diag- nosis of epilepsy from eegs beyond ieds,” Journal of Neural Engineering, vol. 19, no. 6, p. 066017, 2022.
  11. M. Liu, B. Liu, Z. Ye, and D. Wu, “Bibliometric analysis of electroencephalogram research in mild cognitive im- pairment from 2005 to 2022,” Frontiers in Neuroscience, vol. 17, p. 1128851, 2023.
  12. H. B. Hawsawi, P. J. Allen, T. Warbrick, R. St¨ormer, G. Iannotti, F. Grouiller, S. Vulliemoz, and L. Lemieux, “Eeg instrumentation and safety in the mri environment,” in EEG-fMRI: Physiological Basis, Technique, and Ap- plications, pp. 141–166, Springer, 2023.
  13. G. A. Buzzell, Y. Niu, S. Aviyente, and E. Bernat, “A practical introduction to eeg time-frequency principal components analysis (tf-pca),” Developmental Cognitive Neuroscience, vol. 55, p. 101114, 2022.
  14. S. Beniczky, H. Aurlien, J. C. Brøgger, L. J. Hirsch, D. L. Schomer, E. Trinka, R. M. Pressler, R. Wennberg, G. H. Visser, M. Eisermann, et al., “Standardized computer- based organized reporting of eeg: Score–second version,” Clinical Neurophysiology, vol. 128, no. 11, pp. 2334– 2346, 2017.
  15. S. W. Roberson, P. Shah, V. Piai, H. Gatens, A. M. Krieger, T. H. Lucas II, and B. Litt, “Electrocorticogra- phy reveals spatiotemporal neuronal activation patterns of verbal fluency in patients with epilepsy,” Neuropsy- chologia, vol. 141, p. 107386, 2020.
  16. S. Abdulwahab, H. Khleaf, and M. Jassim, “Eeg motor- imagery bci system based on maximum overlap discrete wavelet transform (modwt) and machine learning al- gorithm,” Iraqi Journal for Electrical and Electronic Engineering, vol. 17, no. 2, pp. 38–45, 2021.
  17. R. Bandopadhyay, T. Singh, M. M. Ghoneim, S. Al- shehri, E. Angelopoulou, Y. N. Paudel, C. Piperi, J. Ah- mad, N. A. Alhakamy, M. A. Alfaleh, et al., “Re- cent developments in diagnosis of epilepsy: scope of microrna and technological advancements,” Biology, vol. 10, no. 11, p. 1097, 2021.
  18. A. S. Konar, A. D. Shah, R. Paudyal, M. Fung, S. Baner- jee, A. Dave, V. Hatzoglou, and A. Shukla-Dave, “Quan- titative synthetic magnetic resonance imaging for brain metastases: a feasibility study,” Cancers, vol. 14, no. 11, p. 2651, 2022.
  19. H. Od´een and D. L. Parker, “Magnetic resonance ther- mometry and its biological applications–physical princi- ples and practical considerations,” Progress in nuclear magnetic resonance spectroscopy, vol. 110, pp. 34–61, 2019.
  20. B. Heim, S. Mangesius, F. Krismer, G. K. Wenning, A. Hussl, C. Scherfler, E. R. Gizewski, M. Schocke, R. Esterhammer, A. Quattrone, et al., “Diagnostic accu- racy of mr planimetry in clinically unclassifiable parkin- sonism,” Parkinsonism & Related Disorders, vol. 82, pp. 87–91, 2021.
  21. A. Z. Atiyah and K. H. Ali, “Brain mri images seg- mentation based on u-net architecture,” IJEEE Journal, pp. 21–27, 2021.
  22. J.-G. Yoo, D. Jakabek, H. Ljung, D. Velakoulis, D. van Westen, J. C. Looi, and K. K¨all´en, “Mri morphology of the hippocampus in drug-resistant temporal lobe epilepsy: Shape inflation of left hippocampus and cor- relation of right-sided hippocampal volume and shape with visuospatial function in patients with right-sided tle,” Journal of Clinical Neuroscience, vol. 67, pp. 68– 74, 2019.
  23. A. Massot-Tarr´us and S. M. Mirsattari, “Roles of fmri and wada tests in the presurgical evaluation of language functions in temporal lobe epilepsy,” Frontiers in Neu- rology, vol. 13, p. 884730, 2022.
  24. O. S. Atiyah and S. H. Thalij, “A comparison of coivd- 19 cases classification based on machine learning ap- proaches,” Iraqi Journal for Electrical and Electronic Engineering, vol. 18, no. 1, pp. 139–143, 2022.
  25. E. Tuncer and E. D. Bolat, “Classification of epileptic seizures from electroencephalogram (eeg) data using bidirectional short-term memory (bi-lstm) network ar- chitecture,” Biomedical Signal Processing and Control, vol. 73, p. 103462, 2022.
  26. G. Y. Abbass and A. F. Marhoon, “Iraqi license plate detection and segmentation based on deep learning,” Iraqi Journal for Electrical and Electronic Engineer- ing, vol. 17, no. 2, pp. 102–107, 2021.
  27. E. B. Assi, D. K. Nguyen, S. Rihana, and M. Sawan, “Towards accurate prediction of epileptic seizures: A review,” Biomedical Signal Processing and Control, vol. 34, pp. 144–157, 2017.
  28. J. B. Romaine, M. Pereira Mart´ın, J. R. Salvador Or- tiz, and J. M. Manzano Crespo, “Eeg—single-channel envelope synchronisation and classification for seizure detection and prediction,” Brain Sciences, vol. 11, no. 4, p. 516, 2021.
  29. M. Abdulla and A. Marhoon, “Deep learning and iot for monitoring tomato plant,” Iraqi Journal for Electrical and Electronic Engineering, vol. 19, 2023.
  30. S. A. Thamer and M. A. Alshmmri, “Detection of covid- 19 using cad system depending on chest x-ray and ma- chine learning techniques,” Iraqi Journal for Electrical And Electronic Engineering, vol. 18, no. 2, 2022.
  31. A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learn- ing for electroencephalogram (eeg) classification tasks: a review,” Journal of neural engineering, vol. 16, no. 3, p. 031001, 2019.
  32. M. Natu, M. Bachute, S. Gite, K. Kotecha, A. Vidyarthi, et al., “Review on epileptic seizure prediction: machine learning and deep learning approaches,” Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
  33. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017.
  34. W. H. Park, N. M. F. Qureshi, and D. R. Shin, “An ef- fective 3d text recurrent voting generator for metaverse,” IEEE Transactions on Affective Computing, 2022.
  35. S. K. Jawad and M. Alaziz, “Human activity and ges- ture recognition based on wifi using deep convolutional neural networks,” Iraqi Journal for Electrical And Elec- tronic Engineering, vol. 18, no. 2, 2022.
  36. R. Chai, S. H. Ling, P. P. San, G. R. Naik, T. N. Nguyen, Y. Tran, A. Craig, and H. T. Nguyen, “Improving eeg- based driver fatigue classification using sparse-deep be- lief networks,” Frontiers in neuroscience, vol. 11, p. 103, 2017.
  37. L. Vaˇreka and P. Mautner, “Stacked autoencoders for the p300 component detection,” Frontiers in neuroscience, vol. 11, p. 302, 2017.
  38. A. Ditthapron, N. Banluesombatkul, S. Ketrat, E. Chuangsuwanich, and T. Wilaiprasitporn, “Universal joint feature extraction for p300 eeg classification using multi-task autoencoder,” IEEE access, vol. 7, pp. 68415– 68428, 2019.
  39. N. A. Noori and A. A. Yassin, “Towards for designing in- telligent health care system based on machine learning.,” Iraqi Journal for Electrical & Electronic Engineering, vol. 17, no. 2, 2021.
  40. V. Gabeff, T. Teijeiro, M. Zapater, L. Cammoun, S. Rheims, P. Ryvlin, and D. Atienza, “Interpreting deep learning models for epileptic seizure detection on eeg signals,” Artificial intelligence in medicine, vol. 117, p. 102084, 2021.
  41. Y. Pan, X. Zhou, F. Dong, J. Wu, Y. Xu, S. Zheng, et al., “Epileptic seizure detection with hybrid time-frequency eeg input: a deep learning approach,” Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
  42. W. Zhao, W. Zhao, W. Wang, X. Jiang, X. Zhang, Y. Peng, B. Zhang, G. Zhang, et al., “A novel deep neural network for robust detection of seizures using eeg signals,” Computational and mathematical methods in medicine, vol. 2020, 2020.
  43. A. Abdelhameed and M. Bayoumi, “A deep learning ap- proach for automatic seizure detection in children with epilepsy,” Frontiers in Computational Neuroscience, vol. 15, p. 650050, 2021.
  44. Y. Ru, J. Li, H. Chen, J. Li, et al., “Epilepsy detection based on variational mode decomposition and improved 126 | Assim & Mahmood sample entropy,” Computational Intelligence and Neuro- science, vol. 2022, 2022.
  45. Y. Cao, Y. Guo, H. Yu, and X. Yu, “Epileptic seizure auto-detection using deep learning method,” in 2017 4th International Conference on Systems and Informatics (ICSAI), pp. 1076–1081, IEEE, 2017.
  46. H. S. Nogay and H. Adeli, “Detection of epileptic seizure using pretrained deep convolutional neural network and transfer learning,” European neurology, vol. 83, no. 6, pp. 602–614, 2021.
  47. M. S. Islam, K. Thapa, and S.-H. Yang, “Epileptic-net: an improved epileptic seizure detection system using dense convolutional block with attention network from eeg,” Sensors, vol. 22, no. 3, p. 728, 2022.
  48. X. Tian, Z. Deng, W. Ying, K.-S. Choi, D. Wu, B. Qin, J. Wang, H. Shen, and S. Wang, “Deep multi-view fea- ture learning for eeg-based epileptic seizure detection,” IEEE Transactions on Neural Systems and Rehabilita- tion Engineering, vol. 27, no. 10, pp. 1962–1972, 2019.
  49. P. Detti, “Siena scalp eeg database,” PhysioNet. doi, vol. 10, 2020.
  50. K. Das, D. Daschakladar, P. P. Roy, A. Chatterjee, and S. P. Saha, “Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of eeg sig- nal,” Biomedical Signal Processing and Control, vol. 57, p. 101720, 2020.
  51. G. Choi, C. Park, J. Kim, K. Cho, T.-J. Kim, H. Bae, K. Min, K.-Y. Jung, and J. Chong, “A novel multi-scale 3d cnn with deep neural network for epileptic seizure detection,” in 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–2, IEEE, 2019.
  52. S. Raghu, N. Sriraam, Y. Temel, S. V. Rao, A. S. Hegde, and P. L. Kubben, “Performance evaluation of dwt based sigmoid entropy in time and frequency domains for au- tomated detection of epileptic seizures using svm clas- sifier,” Computers in biology and medicine, vol. 110, pp. 127–143, 2019.
  53. N. J. Stevenson, K. Tapani, L. Lauronen, and S. Vanhat- alo, “A dataset of neonatal eeg recordings with seizure annotations,” Scientific data, vol. 6, no. 1, pp. 1–8, 2019.
  54. P. Swami, B. Panigrahi, S. Nara, M. Bhatia, and T. Gandhi, “Eeg epilepsy datasets,” DOI: https://doi. org/10.13140/RG, vol. 2, no. 14280.32006, 2016.
  55. A. H. Shoeb, Application of machine learning to epilep- tic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology, 2009.
  56. R. G. Andrzejak, K. Schindler, and C. Rummel, “Non- randomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy pa- tients,” Physical Review E, vol. 86, no. 4, p. 046206, 2012.