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

2026-08-03

AI Summary

This study addresses the limited performance of existing automatic sleep staging methods in ambiguous and transitional stages—particularly N1—due to insufficient modeling of fine-grained intra-epoch structures and cross-regional spectral dependencies. To overcome this, the authors propose a dual-stream hierarchical context-aware framework that jointly processes raw time-domain EEG signals and multi-scale time-frequency representations. Convolutional encoders capture waveform details, while Swin Transformers model local spectro-temporal features and long-range dependencies. A bidirectional context module then fuses multi-branch features to explicitly refine both intra-epoch representations and inter-epoch temporal relationships. Evaluated on the Sleep-EDF-20/78 and SHHS datasets, the method achieves state-of-the-art performance, significantly improving robustness and accuracy in identifying N1 and other transitional sleep stages.

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.

Problem

• sleep staging
• ambiguous stages
• transition epochs
• intra-epoch structure
• spectral dependencies

Innovation

• Swin Transformer
• dual-stream architecture
• intra-epoch representation
• time-frequency analysis
• sleep staging