EEG Signal Analysis Approaches for Epileptic Seizure Event Prediction Using Deep Learning

Epilepsy is classified as one of the three most prevalent neurological disorders, alongside strokes and migraines. It is characterized by the occurrence of epileptic seizures that can be detected in electroencephalographs (EEGs) shortly before and during seizures, since they cause abnormal brain activity. Traditionally, the prediction of epileptic events is done by experienced neurologists who can interpret electroencephalography data. However, more consistent and automated methods are needed for early prediction of epileptic seizures, to provide immediate treatment to patients and prevent injury risks. Deep learning has been employed towards this direction, as a costly and feasible alternative for epileptic seizures’ early prediction based on EEG data. This work reviews the state of the art in deep learning applied to epileptic seizure prediction and summarizes all recent approaches. Available datasets are provided, advantages and limitations in employing deep learning techniques are presented, and the most promising models are delineated.