Prediction models for epilepsy detection on the EEG signal

Abstract

Epilepsy is a neurological illness characterized by abnormal brain activity, resulting in seizures or episodes of odd behavior, feelings, and in some cases, loss of awareness. In this work, we propose a comparison between three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and bidirectional LSTM for epileptic seizure detection using EEG data which is the most common technique used for Epilepsy diagnosis. The objective of this work is to define the most suitable model for this sensitive task and to reach the highest possible accuracy. To evaluate the performance of the proposed methods, many experiments are conducted to study the effect of some parameters and using two categorical combinations of an EEG dataset. As a result, we reached a prediction accuracy of 90.26% with CNN, 86.17% with LSTM but the Bi-LSTM model consistently outperformed the other models reaching more than 98% accuracy. Finally, these results demonstrate the possibility of detecting the epileptic seizures while maintaining model interpretability, which may contribute to a better understanding of brain dynamics and enhance predictive performances