A Comprehensive Review of EEG-Based Seizure Detection Techniques

• Published in: IEEE access Vol. 13; pp. 103531 - 103564
• Main Authors: Sudhakar Jebaraj, Goldwyn, Elango, Konguvel
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Electrodes; electroencephalogram; Electroencephalography; Epilepsy; epileptiform activity; machine learning algorithm; Mental health; Monitoring; Mortality; neuro imaging; neurophysiology; Pediatrics; Recording; Reviews; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2025.3578991

The automated classification and prediction of epileptic seizures using electroencephalography (EEG) remains a dynamic and critical area of interdisciplinary research, situated at the intersection of neuroscience and computational intelligence. Despite considerable progress in EEG signal acquisition, preprocessing methodologies, and the application of machine learning algorithms, significant challenges persist due to the inherent complexity and inter-subject variability observed in seizure patterns. This review aims to provide guidance for new researchers in this field, starting with the fundamental aspects of epilepsy, encompassing its diverse clinical presentations (syndromes), associated health conditions (comorbidities), and characteristic EEG manifestations. Furthermore, it provides a detailed analysis of recent advancements in automated seizure classification and prediction systems. We examine crucial preprocessing techniques, feature extraction strategies, and the application of both established statistical models and contemporary deep learning architectures, including innovative approaches such as image-based representations of EEG data and transfer learning paradigms. A comprehensive compilation of EEG datasets, spanning neonatal, pediatric, adult, and animal studies, is systematically presented, with a focus on their annotation schemes, and the underlying research objectives. Additionally, we discuss post-processing methodologies aimed at refining prediction accuracy, considerations for real-time hardware implementation to facilitate practical application, and emerging trends such as explainable artificial intelligence (XAI) to enhance the interpretability of AI-driven decisions, federated learning to enable collaborative model development across institutions, and neuromorphic computing inspired by the brain's architecture. By discussing the pros and cons of different methods, current limitations, and ethical considerations, this review seeks to help new researchers develop better EEG-based seizure classification and prediction systems with real-world impact for patients.