A Multi-View Graph Convolutional Network with Context Gating for Frequency-Aware Sleep Stage Classification

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Background: Sleep staging is important for assessing sleep architecture and supporting the diagnosis of sleep-related disorders. Manual scoring is time-consuming and subject to inter-scorer variability, while joint modeling of EEG channel relationships and frequency characteristics remains challenging. Methods: We propose DCMG-Net (dual-branch CNN and context-gated multi-view graph network), a frequency-aware network combining multi-view graph convolution with context gating for five-class sleep staging using EEG alone. Multiscale convolutions and attention extract channel features. Functional, globally learnable, and four physiological frequency-band adjacency matrices define six graph views, whose representations are adaptively fused by context gating. Results: On Sleep-EDF-20, ISRUC-S3, and the Sleep Heart Health Study (SHHS), overall accuracies were 91.2%, 90.0%, and 86.3%; macro-F1 scores were 84.4%, 88.7%, and 77.2%; and Cohen’s κ values were 0.88, 0.87, and 0.81, respectively. Macro-F1 exceeded the highest values reported by the compared literature methods by 1.9, 4.2, and 0.7 percentage points, respectively. In the ablation experiments, gating improved macro-F1 over direct concatenation by 6.1 and 2.3 percentage points on ISRUC-S3 and SHHS. Conclusions: Frequency-band topology and context gating jointly exploit EEG frequency information and channel relationships to improve multi-view classification, providing a methodological basis for portable sleep monitoring and computer-assisted clinical scoring.