Multichannel EEG based Hybrid Gated Separable Convolution and Transformer Encoder for Sleep Stage Epilepsy Prediction

Sleep stage epilepsy represents a critical neurological disorder where seizures occur during distinct phases of sleep, disrupting normal brain function and sleep architecture.Automated and reliable prediction of sleep-related epilepsy is therefore essential to support early diagnosis, long-term monitoring, and clinical decision-making.This study presents a novel hybrid Gated Separable Convolution Network (GSCN)-Transformer encoder model designed to predict sleep stage epilepsy from multichannel EEG recordings.The architecture integrates two complementary modules: the GSCN block extracts localized temporal features through depthwise separable convolutions and a gating mechanism, while the Transformer encoder captures long-range dependencies and inter-channel relationships using multi-head self-attention (MHSA).This integration ensures that both fine-grained temporal details and broader contextual dependencies are effectively represented, resulting in a unified feature space optimized for classification.The proposed framework was evaluated on the Siena Sleep EEG dataset using a rigorously designed preprocessing pipeline and stratified train-test splitting, achieving a high classification accuracy of 98.78%, with precision, recall, and F1-score of 98.79%.Subject-wise cross-validation further confirmed the model's robustness, yielding a mean accuracy exceeding 98% with narrow confidence intervals.Statistical significance analysis using DeLong's and McNemar's tests demonstrated that the proposed model significantly outperforms conventional machine learning and deep learning baselines.Additionally, external benchmarking on the CHB-MIT Scalp EEG dataset without retraining achieved an accuracy of 95.2%, confirming great cross-dataset generalization.The results confirm the framework's suitability for clinical decision support, continuous sleep monitoring and integration into real-time healthcare applications for epilepsy management.