Epileptic Seizure Detection from EEG Signal Using Progressive Channel Selection and Deep Learning

Abstract

Epileptic Seizures (ES), marked by abrupt abnormal electrical discharges in the brain, represent the primary symptom of epilepsy, making timely and accurate detection critical because of their unpredictable recurrence and potentially severe consequences. The Electroencephalogram (EEG), a widely used, non-invasive, and economical method for monitoring brain activity, plays a key role in automated ES detection through Machine Learning (ML) and Deep Learning (DL) techniques. Most existing ML/DL approaches typically utilize all available EEG channels or apply patient-specific channel selection to maximize detection performance. This study investigates a novel Progressive Channel Selection (PCS) framework designed to identify and retain only the most informative EEG channels across patients. The proposed method progressively selects channels in multiple stages according to their contribution to classification accuracy, ensuring that only the most influential channels are preserved. Experimental evaluation was performed using both Neural Network (NN) and Convolutional Neural Network (CNN) models on the CHB-MIT dataset. The CNN model achieved an accuracy of 98.99% while utilizing fewer than half of the available EEG channels. Although some existing approaches report slightly higher accuracy by employing all channels, the proposed method provides a more effective trade-off between detection accuracy and channel efficiency. The identification of informative EEG channels could support hardware simplification and may facilitate low-cost EEG-based ES detection systems.