Abstract Purpose Reliable classification of pediatric sleep stages remains a challenge due to the complexity of polysomnographic (PSG) data and variability in manual scoring. This study investigates whether an attention-augmented CNN-BiGRU hybrid architecture can achieve accurate and interpretable four-class sleep stage classification using multi-channel EEG, while also identifying redundant channels to support sensor simplification. Methods We developed a hybrid architecture integrating channel attention, convolutional layers, and bidirectional gated recurrent units (BiGRUs) for four-class sleep staging (Wake, Stage 1, Stage 2, REM) using seven EEG channels from the NCH Sleep Databank. Data preprocessing included Butterworth filtering, 30-second epoch segmentation, z-score normalization, temporal augmentation, and class relabeling tailored to pediatric sleep. An ablation study examined the contribution of individual EEG channels, and explainability methods (SHAP, Grad-CAM) were employed to enhance model interpretability. Results The proposed model achieved 84.41% accuracy, a macro F1-score of 0.84, and an AUC of 0.9615. Ablation analysis indicated redundancy in the C3-M2 and O2-M1 channels, highlighting the feasibility of reduced-channel acquisition. Explainability techniques identified the most influential EEG channels and temporal regions relevant to classification. Conclusions The CNN-BiGRU hybrid framework provides accurate and interpretable pediatric sleep staging from multi-channel EEG. The approach supports scalable automation of PSG analysis, reduces dependency on redundant sensors, and offers clinical utility for diagnosing pediatric sleep disorders such as OSA.
Nargundkar et al. (Mon,) studied this question.