Expert-distilled spectrogram-attention SAI3C network for robust IIIC EEG pattern recognition

초록

Ictal-Interictal-Injury Continuum (IIIC) patterns are crucial indicators of epilepsy-related disorders, where early detection is essential for diagnosis and treatment. We propose a deep-learning approach for IIIC classification from elec-troencephalogram (EEG). For efficient feature extraction, we compress 20 EEG leads into 4 using the Banana montage and convert signals to Mel-spectrograms to capture time-frequency-amplitude characteristics. Our architecture comprises a Symmetry-Aware Intelligent IIIC Classifier Network (SAI3CNet) with a Spectrogram-Attention (SpAtt) block and a Lead Relationship Encoder (LRE) that models inter-lead symmetry and correlations; we further adopt an Expert-Distilled EEG learning strategy to reflect expert uncertainty in ambiguous brain-wave patterns. In clinically realistic class-imbalanced settings, the method outperformed strong baselines, reducing over-prediction of the "Other" class and improving minority-pattern recognition such as LRDA while maintaining well-calibrated probabilities. These properties highlight the system's technical robustness and potential utility as a supportive screening tool. By pre-screening continuous EEG to prioritize high-risk segments and highlighting symmetry-aware saliencies, the proposed method aims to assist specialists in rapid tri-age and reduce review workload, thereby potentially facilitating more timely assessments in ICU and epilepsy-monitoring workflows

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