SWINSleepNet: A Hierarchical Context-Aware Framework for Sleep Staging (v2)

• URL: http://arxiv.org/abs/2608.02183v1
• Date: Mon, 03 Aug 2026 13:07:49 GMT
• Title: SWINSleepNet: A Hierarchical Context-Aware Framework for Sleep Staging (v2)
• Abstract summary: Traditional epoch-level encoders fail to extract subtle temporal microstructures and intra-epoch cross-region interactions.<n>We propose SwinSleepNet, a hierarchical context-aware dual-stream framework that separately optimize intra-epoch representation learning and inter-epoch contextual modeling.<n>Our method achieves competitive overall performance, and exhibits stronger robustness and stability on difficult N1 stages and transitional epochs.
• Score: 3.911341147858216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic sleep staging is a critical role in sleep disorder diagnosis, sleep quality assessment, and long-term health monitoring; however, existing approaches suffer poor performance on ambiguous and transition-related sleep stages, caused by inadequate modeling of fine-grained intra-epoch structures and complex cross-region spectral dependencies. Traditional epoch-level encoders commonly fail to extract subtle temporal microstructures and intra-epoch cross-region interactions, resulting in unsatisfactory recognition accuracy for hard categories such as the N1 stage. To tackle these drawbacks, we propose SwinSleepNet, a hierarchical context-aware dual-stream framework that separately optimizes intra-epoch representation learning and inter-epoch contextual modeling. Concretely, we characterize each sleep epoch from two complementary perspectives: raw time-domain EEG signal and its time-frequency transformation. The time-domain branch adopts convolutional encoders to capture fine waveform temporal details, and the time-frequency branch uses Swin Transformer to extract local spectro-temporal features, hierarchical multi-scale information and long-range spatial dependencies. The multi-branch extracted features are fused into integrated embeddings, which are optimized by a bidirectional context module to capture cross-epoch temporal dependencies for final sleep stage classification. Comprehensive experiments on Sleep-EDF-20, Sleep-EDF-78 and SHHS datasets verify that our method achieves competitive overall performance, and exhibits stronger robustness and stability on difficult N1 stages and transitional epochs. The results prove that optimized intra-epoch representation learning based on hierarchical architecture greatly benefits automatic sleep staging tasks.

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