A Novel Unsupervised Approach for Accurate Epileptic Seizure Detection

Epileptic seizures, unpredictable and potentially fatal, pose a significant threat to patients' health. Timely detection through electroencephalography (EEG) is crucial, but the high variability of seizure patterns and signal artifacts presents challenges. Existing automated detection methods often rely on supervised learning, which may struggle with these difficulties. This article introduces a novel fully unsupervised approach, the Deep Gaussian Mixture Model (DGMM), that leverages the power of deep clustering (DC) to address these challenges. DGMM employs a deep autoencoder (AE) to extract essential features from EEG data in a reduced-dimensional space. It then uses a Gaussian Mixture Model (GMM) to identify seizure patterns within this space without human intervention, ensuring efficient processing and analysis. Evaluated on a large real-world dataset, DGMM demonstrates substantial performance improvements compared to existing methods. In contrast to conventional supervised methods, our approach achieves a significant reduction in processing time, demonstrating its computational efficiency. This promising result suggests that DGMM holds significant potential for enhancing automated seizure detection and improving patient care.