MSF-SleepNet:Multi-Stream Fusion Network with Contrastive Learning for Sleep Stage Classification

Sleep stage classification is significant for sleep specialists to evaluate sleep quality and diagnose sleep disorders. Machine learning and deep learning technologies are widely employed to build automatic sleep stage classification models. However, how to make full use of and integrate multiple heterogeneous information such as unlabeled information, topological information, frequency information and neighboring information to improve classification is still an open problem. To address this issue, we propose a multi-stream fusion network named MSF-SleepNet for sleep stage classification, with contrastive learning to combine spatial, temporal, and spectral features. Firstly, we design a contrastive learning framework to learn general features from unlabeled information. On this basis, we apply graph structure learning, Chebyshev graph convolution and temporal convolution to capture spatial-temporal features from topological information of human body in non-Euclidean space and transition rules among sleep stages. Secondly, we utilize the short-time Fourier transform and Gate Recurrent Unit to gain spectral-temporal features from frequency information of different time series signals in Euclidean space and neighboring information of adjacent signal segments. Finally, fusing spatial-temporal features and spectral-temporal features can further enhance the performance of sleep stage classification. Experimental results on publicly available datasets of ISRUC-S3 show that our method is more effective in integrating heterogeneous information and achieves better performance than existing state-of-the-art methods.