Contrastive Learning for Sleep Staging based on Inter Subject Correlation

• URL: http://arxiv.org/abs/2305.03178v1
• Date: Fri, 5 May 2023 12:16:01 GMT
• Title: Contrastive Learning for Sleep Staging based on Inter Subject Correlation
• Abstract summary: This paper presents the MViTime model that have been used in sleep staging study. We implement the inter-subject correlation theory through contrastive learning to address the cross-subject problem in sleep stage classification.
• Score: 2.092935059855151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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