EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 20
• Main Authors: Ahmad, Ijaz, Wang, Xin, Zhu, Mingxing, Wang, Cheng, Pi, Yao, Khan, Javed Ali, Khan, Siyab, Samuel, Oluwarotimi Williams, Chen, Shixiong, Li, Guanglin
• Format: Journal Article
• Language: English
• Published: United States Hindawi 17.06.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Brain research; Citation indexes; Convulsions & seizures; Criteria; Data collection; Datasets; Deep Learning; Diagnosis; Disease; EEG; Electroencephalography; Electroencephalography - methods; Environmental factors; Epilepsy; Epilepsy - diagnosis; Feature extraction; Humans; Keywords; Learning algorithms; Machine learning; Mathematical models; Nervous system diseases; Neurological diseases; R&D; Research & development; Review; Reviews; Seizures; Seizures (Medicine); Seizures - diagnosis; Signal Processing, Computer-Assisted; Statistical analysis; Statistical methods; Support Vector Machine; Systematic review; Technology application; Tomography; Workloads; China; Germany
• DOI: 10.1155/2022/6486570
• ISSN: 1687-5265; 1687-5273

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG’s noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG’s low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.