Abstract Epilepsy is a chronic neurological disorder characterized by recurrent seizures that can lead to death. Seizure treatment usually involves antiepileptic drugs and sometimes surgery, but patients with drug-resistant epilepsy often remain effectively untreated owing to the lack of targeted therapies. The development of a reliable technique for detecting and predicting epileptic seizures could significantly impact clinical treatment protocols and the care of patients with epilepsy. Over the years, researchers have developed various computational techniques using scalp electroencephalography (EEG), intracranial EEG, and other neuroimaging modalities, evolving from traditional signal processing methods (e.g., wavelet transforms and template matching) to advanced machine learning (ML, e.g., support vector machines and random forests) and deep learning (DL) algorithms (e.g., convolutional neural networks, recurrent neural networks, transformers, graph neural networks, and hybrid architectures). This review provides a detailed examination of epileptic seizure detection and prediction, covering the key aspects of signal processing, ML algorithms, and DL techniques applied to brainwave signals. We systematically categorized the techniques, analyzed key research trends, and identified critical challenges (e.g., data scarcity, model generalizability, and real-time processing). By highlighting the gaps in the literature, this review serves as a valuable resource for researchers and offers insights into future directions for improving the accuracy, interpretability, and clinical applicability of EEG-based seizure detection systems.
Slama et al. (2025) studied this question.