Sleep stage classification is a significant measure in assessing sleep quality and diagnosing sleep disorders. Many researchers have investigated automatic sleep stage classification methods and achieved promising results. However, these methods ignored the heterogeneous information fusion of the spatial-temporal and spectral-temporal features among multiple-channel sleep monitoring signals. In this study, we propose an interpretable multi-stream fusion network, named MSF-SleepNet, for sleep stage classification. Specifically, we employ Chebyshev graph convolution and temporal convolution to obtain the spatial-temporal features from body-topological information of sleep monitoring signals. Meanwhile, we utilize a short time Fourier transform and gated recurrent unit to learn the spectral-temporal features from sleep monitoring signals. After fusing the spatial-temporal and spectral-temporal features, we use a contrastive learning scheme to enhance the differences in feature patterns of sleep monitoring signals across various sleep stages. Finally, LIME is employed to improve the interpretability of MSF-SleepNet. Experimental results on ISRUC-S1 and ISRUC-S3 datasets show that MSF-SleepNet achieves competitive results and is superior to its state-of-the-art counterparts on most of performance metrics.

Keywords: Chebyshev graph convolution; Contrastive learning; Fusion network; Model interpretability; Sleep stage classification.

© 2025. The Author(s).

睡眠阶段分类是评估睡眠质量及诊断睡眠障碍的重要指标。许多研究人员调查了自动睡眠阶段分类方法，并取得了令人满意的结果。然而，这些方法忽略了多通道睡眠监测信号中空间-时间与频谱-时间特征的异构信息融合。本研究提出了一种可解释的多流融合网络（MSF-SleepNet），用于睡眠阶段分类。具体来说，我们采用切比雪夫图卷积和时间卷积来获取从睡眠监测信号中的身体拓扑信息的空间-时间特征。同时，我们利用短时傅里叶变换和门控循环单元从睡眠监测信号中学习频谱-时间特征。融合空间-时间和频谱-时间特征后，采用对比学习方案增强不同睡眠阶段下睡眠监测信号的特征模式差异性。最后，使用LIME来提高MSF-SleepNet的可解释性。在ISRUC-S1和ISRUC-S3数据集上的实验结果表明，MSF-SleepNet实现了具有竞争力的结果，并且在大多数性能指标上优于最先进的方法。

关键词：切比雪夫图卷积；对比学习；融合网络；模型可解释性；睡眠阶段分类。