文献类型：期刊文献

英文题名：Contrastive Learning forSleep Staging Based onInter Subject Correlation

作者：Zhang, Tongxu[1]; Wang, Bei[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2023

卷号：14256 LNCS

起止页码：343

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20230177565)

语种：英文

外文关键词：Sleep research

摘要：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 has 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. The code can be accessed through: https://github.com/jukieCheung/MViTime. ? 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.