Deep Learning Approaches for Epileptic Seizure Prediction: A Review

Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an epileptic patient due to sudden seizure onset that may cause loss of consciousness. Seizures are periods of aberrant brain activity patterns. Early prediction of an epileptic seizure is critical for those who suffer from it as it will give them time to prepare for an incoming seizure and alert anyone in their close circle of contacts to aid them. This has been an active field of study, powered by the decreasing cost of non-invasive electroencephalogram (EEG) collecting equipment and the rapid evolution of Deep Learning (DL) algorithms. This review paper offers the most recent evaluations of contemporary DL techniques for predicting epileptic seizures with a lot of emphasis on pre-processing, feature extraction and the classification techniques implemented many of which depend on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) as well as the different datasets used. The study compares the claimed sensitivity and false alarm rate to conclude the described methodologies and their limitations.