基于心率变异性的自动睡眠分期及其脑电图验证

doi:10.3969/j.issn.0258-8021. 2024. 01.03

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Abstract Sleep disorders seriously affect life quality, therefore, early sleep monitoring is important for the prevention and diagnosis of sleep diseases. In this paper, we proposed a portable polysomnography and completed in-home sleep data collection for 103 nights by this system, including EEG, EOG, EMG and ECG signals. Time-domain, frequency, and nonlinear features were extracted from the RR intervals of the synchronously acquired ECG data, and up to 426 heart rate variability (HRV) features were combined to construct models based on the Xgboost algorithm to predict wake, non-rapid eye movement I(N1), non-rapid eye movement II (N2), non-rapid eye movement III(N3), and rapid eye movement (REM) stages of sleep with five-classification (wake, N1, N2, N3, and REM), three-classification (wake+N1, REM, N2+N3), and two-classification (wake, N1+N2+N3+REM), and to validate them with the EEG sleep staging labels. Among these, the accuracy of the five-classification, three-classification and two-classification test results reached 84.0%, 89.1% and 95.2%, respectively, and the F1-score reached 83.2%, 88.9% and 94.9%, which was the best performance among other model studies of this kind. It indicated that HRV had good correlation with sleep stages, and the HRV-based algorithmic models constructed based on the data collected from portable devices identified the sleep states well.

Key words： heart rate variability electroencephalogram polysomnography Xgboost automatic sleep staging

Received: 01 November 2022

PACS:

R318

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	Articles by authors
	Ying Shaofei
	Qin Daiyou
	Xie Jiaxin
	Gao Dongrui
	Qin Yun
	Liu Tiejun

Cite this article:

Ying Shaofei,Qin Daiyou,Xie Jiaxin, et al. Heart Rate Variability Based Automatic Sleep Staging and its Validation with EEG[J]. Chinese Journal of Biomedical Engineering, 2024, 43(1): 18-26.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2024. 01.03 OR http://cjbme.csbme.org/EN/Y2024/V43/I1/18

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