Machine and deep learning methods for epileptic seizure recognition using EEG data: A systematic review
Abstract
Epilepsy is a neurological disorder affecting millions worldwide, characterized by recurrent and unpredictable seizures. Electroencephalography (EEG) is a widely used tool for seizure diagnosis, but the complexity and variability of EEG signals make manual analysis challenging. Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful methods for automated Epileptic Seizure (ES) detection, classification, and prediction. However, questions remain regarding their effectiveness, interpretability, and clinical applicability. This systematic review critically examines ML and DL approaches applied to EEG-based seizure recognition, highlighting key challenges such as feature extraction, dataset selection, and model generalization. We analyze peer-reviewed studies from 2013 to 2023, sourced from the PubMed database, to compare various methodologies and evaluate their performance. Unlike prior reviews that focus on a single aspect of seizure recognition, this work provides a comprehensive overview of detection, classification, and prediction tasks. We also discuss the strengths and limitations of different ML and DL models, emphasizing the trade-offs between computational complexity, accuracy, and real-world implementation. Furthermore, this study outlines emerging trends, including the integration of explainable AI, transfer learning, and privacy-preserving techniques such as federated learning. By synthesizing the latest advancements, this review serves as a…
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Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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