In recent years, multitudes of researches have applied deep learning to automatic sleep stage classification. Whereas actually, these works have paid less attention to the issue of cross-subject in sleep staging. At the same time, emerging neuroscience theories on inter-subject correlations can provide new insights for cross-subject analysis. This paper presents the MViTime model that have been used in sleep staging study. And we implement the inter-subject correlation theory through contrastive learning, providing a feasible solution to address the cross-subject problem in sleep stage classification. Finally, experimental results and conclusions are presented, demonstrating that the developed method has achieved state-of-the-art performance on sleep staging. The results of the ablation experiment also demonstrate the effectiveness of the cross-subject approach based on contrastive learning.

翻译：近年来，大量研究将深度学习应用于自动睡眠分期分类。然而，这些工作对睡眠分期中的跨被试问题关注不足。与此同时，新兴的神经科学关于被试间相关性的理论可为跨被试分析提供新视角。本文提出在睡眠分期研究中应用的MViTime模型，并通过对比学习实现被试间相关性理论，为睡眠分期分类中的跨被试问题提供可行方案。最后，实验结果与结论表明，所提方法在睡眠分期任务上达到了目前最优性能。消融实验结果也证实了基于对比学习的跨被试方法的有效性。