EEG-Based Automated Epileptic Seizure Detection using AI: A Comprehensive Review

Epileptic seizures, which are caused by abnormal neuronal bursts, are a major, neurological disorder, which has considerable clinical ramifications. The gold standard in diagnosis of seizures is electroencephalography (EEG); however, the traditional manualized interpretation of EEG is highly labor-intensive and has an extremely high inter-observer variability. The newer applications in the field of artificial intelligence (AI) that include signal-processing, classical machine learning, and deep-learning approaches provide valid paradigms of the automatic and accurate detection of the seizures. In this review, a detailed analysis of the EEG-based epileptic seizure detection, as well as the classification of the seizure based on the International League Against Epilepsy (ILAE), the etiology of the seizures, the treatment, signal-preprocessing, and the feature-extraction models, will be done. We also compare and contrast between classical and deep-learning methods, clinical processes, and performance indicators systematically. Lastly, the explainable AI, transformer-based architectures, and clinical integration in real-time patient-centric seizure management are highlighted in terms of future perspectives.