EEG-based epileptic seizure detection using deep learning techniques: A survey

Abstract

Epilepsy is a complex neurological disorder marked by recurrent seizures, often stemming from abnormal discharge of the brain. Electroencephalogram (EEG) captures temporal and spatial shifts in cerebral electrical activity, holding pivotal diagnostic and therapeutic value for epilepsy. Deep learning techniques have made remarkable progress in EEG-based seizure detection over recent years. This review is dedicated to exploring seizure detection approaches based on deep learning, focusing on three distinct avenues. Primarily, we delve into the application of canonical deep learning methods in epilepsy detection. Subsequently, a more in-depth study was conducted on the hybrid models of deep learning. Next, the third is the integration of deep learning and traditional machine learning strategies. Finally, the challenges and future prospects related to this topic are put forward. The uniqueness of this review lies in its novel and comprehensive perspective on the latest research on deep learning-based epilepsy detection by systematically classifying methods, visualizing research progress, and addressing challenges and gaps in current research. It can provide valuable guidance for researchers who want to delve into the field of epileptic seizure detection based on EEG signals.