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Purpose <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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Explainable attention-augmented CNN-BiGRU hybrid model for automated sleep stage classification in pediatric subjects with obstructive sleep apnea

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月18日

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  • Sneha Nargundkar,
  • Supriya Shanbhag,
  • Veena Desai

摘要

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.