Multi-Scale Residual Graph Learning with Contextual Memory for Sleep Staging

Sleep is a vital physiological process. Although deep learning-based automated sleep staging methods have achieved significant progress, the studies still face limitations in fully mining spatio-temporal graph dependencies. Existing methods face challenges in capturing implicit spatial topology among multi-channel signals and efficiently fusing multi-level features in long-sequence modeling. To address these challenges, this paper proposes a novel hybrid deep learning framework for sleep staging that progressively and explicitly integrates spatial topology modeling, cross-level feature fusion, and global temporal aggregation. By decoupling shallow and deep spatial features and hierarchically organizing feature interactions, the proposed framework effectively captures implicit spatial dependencies while maintaining computational efficiency in long-sequence modeling. Experimental results on ISRUC-S1, ISRUC-S3, and Sleep-EDF-153 show competitive accuracy under the reported subject-level evaluation protocol (83.1%/80.1%, 84.0%/83.8%, and 85.9%/83.7% Accuracy/F1, respectively). In an additional MIT-BIH evaluation, the model achieves 75.1% accuracy and 74.5% F1-score. The implementation-level runtime measurements provide evidence of deployment potential, while repeated-seed and matched-baseline studies remain necessary before making stronger efficiency or real-time claims.