Enhancing Epileptic Seizure Identification by Exploring Swin Transformer Integration within Conformer Architecture.

This study proposes a novel enhancement to the Conformer architecture for epileptic seizure identification by replacing the standard Vision Transformer (ViT) with the Swin Transformer. The proposed Swin-Conformer model leverages the hierarchical patch merging and shifted-window self-attention mechanisms of the Swin Transformer to better capture both local electrophysiological patterns and long-range temporal dependencies in electroencephalogram (EEG) signals. To handle the significant class imbalance inherent in epileptic EEG data, a weighted focal loss function is employed during training. The model is evaluated on the CHB-MIT dataset through stratified 10-fold cross-validation and validated on the independent Bonn EEG dataset. Experimental results demonstrate that the proposed model achieves accuracy of 99.24%, specificity of 99.55%, and sensitivity of 98.47% on the segment-based evaluation, as well as an event-based sensitivity of 99.50%, outperforming the original Conformer and state-of-the-art baseline methods. Ablation studies confirm that the performance gains originate from the Swin Transformer's hierarchical multi-scale feature representation and efficient local-global attention mechanism. Statistical analysis confirms that the improvements are statistically significant ([Formula: see text]).