Siamese Network-Based Feature Transformation for Improved Automated Epileptic Seizure Detection

Abstract

Epilepsy is a common electrophysiological disorder of the brain, detected mainly by electroencephalogram (EEG) signals. Since correctly diagnosing epilepsy seizures by monitoring the EEG signal is very tedious and time-consuming for a neurologist, a growing number of studies have been conducted on developing automated epileptic seizure detection (AESD). In this work, a novel supervised model is proposed for AESD. Initially, the EEG signals are collected from Bonn University EEG (BU-EEG) database. Then, empirical mode decomposition and feature extraction (combination of entropy, frequency, and statistical features) are applied to extract the features. Furthermore, Siamese network is utilized to lessen the number of extracted features and obtain the most discriminative features. Then, these features are exploited to classify seizure and non-seizure EEG signals by using a support vector machine classifier. This paper examines the Siamese network’s contribution as a learning-based feature transformation in improving seizure detection performance. The numerical results confirm that the proposed framework can achieve a perfect classification performance ( $100\%$ accuracy). This approach can constructively help doctors to detect epileptic seizure activity and reduce their workload.