Epileptic Seizure Prediction using EEG Images

Epilepsy is a chronic neurological disorder where seizures occur due to the transient and unexpected electrical disturbances of the brain. Epilepsy affects the quality of life of patients, making them unable to carry out essential day to day activities. Predicting an impending seizure will help them to make effective interventions. In this work, we proposed an automated system to classify the EEG data into ictal, nonictal, and pre-ictal classes using ResNET-50, a subclass of convolutional neural networks, by converting 1D EEG data into 2D EEG images. Using this novel approach, the proposed model predicts an oncoming seizure with an accuracy of 94.98%. This shows that deep residual network is a promising approach for analyzing EEG data to predict epileptic seizures.