基于单通道脑电的跨域自动睡眠分期

doi:10.3969/j.issn.0258-8021.2025.04.003

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Abstract Sleep staging is of great significance for diagnosis and treatment of sleep problems. Currently, existing deep sleep staging networks have limitations including low data efficiency and data distribution differences, resulting in a decrease in the model′s performance trained on the actual data from training sets. To address this problem, this paper proposed an adversarial domain adaptation network for cross-domain automatic sleep staging using single-channel EEG. This network used a feature extractor to extract EEG features, and at the same time used a non-shared attention mechanism to preserve key information of specific domains, combined a domain discriminator to align the source and target domains, and solved the class-level alignment problem through a dual-stage classifier based on iterative self-training. To verify the reliability of the proposed network, 39, 42, and 44 records were randomly selected from three public datasets of Sleep-EDF-20, SHHS1, and SHHS2 respectively. Experiments conducted on the six generated cross-domain scenarios showed that the proposed network achieved an average accuracy of 74.29% and an average MF1 value of 61.95%. When compared with the performance of other baseline models, the average accuracy of the proposed network was at least 2.01% higher than that of the existing baseline models, and the average MF1 score was at least 2.22% higher. This method provided a solution for addressing the domain shift problem in the sleep staging task.

Key words： single channel EEG cross-domain automatic sleep staging adversarial domain adaptation

Received: 30 January 2024

PACS:

R318

Corresponding Authors: ^*E-mail: lijin1997@snnu.edu.cn

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	Articles by authors
	Zhu Mengyuan
	Quan Ruomeng
	Qiang Ning
	Hu Jing
	Feng Feilong
	Li Jin

Cite this article:

Zhu Mengyuan,Quan Ruomeng,Qiang Ning, et al. Cross Domain Automatic Sleep Staging Based on Single Channel EEG[J]. Chinese Journal of Biomedical Engineering, 2025, 44(4): 406-415.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2025.04.003 OR http://cjbme.csbme.org/EN/Y2025/V44/I4/406

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