Bridging Feature-Based and Temporal Learning for EEG-Based Epileptic Seizure Detection

Abstract

A novel lightweight comparative ML-DL framework for detecting seizures with EEG signals recorded using the UCI Epileptic Seizure Recognition Dataset is presented in this article. The data collected from the experiments was adjusted to form a binary classification problem, where the elements of the dataset were divided into seizure versus non-seizure. The number of features was reduced from 178 to 148 with a variance feature reduction method while maintaining the discriminative quality of the features. Four traditional machine learning models were compared with a Long Short-Term Memory (LSTM) network model to capture the temporal aspects of the EEG signal. The Random Forest algorithm produced an overall accuracy of 97.08%, while the LSTM produced an accuracy of 98%. This finding is indicative that when a framework is developed with an LSTM and temporal modeling capabilities, the performance increases, therefore making it more suitable for real-time seizure detection.