Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer

Epilepsy manifests as a chronic neurological condition marked by recurrent seizures. Recent advances in computational analysis of Electroencephalography (EEG) signals have enabled new possibilities for identifying ictal events in extended recordings. This work develops a novel deep learning architecture that simultaneously resolves two fundamental challenges in automated seizure detection: comprehensive feature representation and class distribution imbalance. First, a multibranch neural network structure is proposed to process EEG signals across varying spectral and temporal resolutions. Then, an attention-based feature refinement mechanism is utilized to automatically emphasize clinically relevant signal characteristics. Finally, a modified loss function is leveraged to incorporate class-specific margin adjustments to handle data imbalance scenarios. Analysis of scalp EEG recordings yields detection accuracy with 96.06% sensitivity and 98.50% specificity, and false detection rate (FDR) is maintained at a low level of 0.34 events per hour. When applied to intracranial EEG data, the algorithm demonstrates similar efficacy (95.90% sensitivity, 98.65% specificity) with further reduced false detections (0.18/h). The consistent efficacy validated on diverse EEG modalities (scalp and intracranial) supports its clinical utility as a practical diagnostic tool.