Novel type of unperturbed sleep monitoring scheme under pillow based on hidden Markov model.
10.7507/1001-5515.201703059
- Author:
Xiang LI
1
;
Yong LIU
2
;
Pengbin CHEN
2
;
Jiewei WU
1
;
Han ZHANG
3
,
4
,
5
Author Information
1. Institute of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, P.R.China.
2. Guangzhou SENVIV Technology Co. Ltd, Guangzhou 510006, P.R.China.
3. Institute of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, P.R.China
4. Guangzhou SENVIV Technology Co. Ltd, Guangzhou 510006, P.R.China
5. Guangdong Provincial Engineering Research Center for Cardiovascular Individual Medicine & Big Data, Guangzhou 510006, P.R.China.zhanghan@scnu.edu.cn.
- Publication Type:Journal Article
- Keywords:
ensemble empirical mode decomposition;
heart rate variability;
hidden Markov model;
sleep stages
- From:
Journal of Biomedical Engineering
2018;35(2):280-289
- CountryChina
- Language:Chinese
-
Abstract:
Sleep status is an important indicator to evaluate the health status of human beings. In this paper, we proposed a novel type of unperturbed sleep monitoring system under pillow to identify the pattern change of heart rate variability (HRV) through obtained RR interval signal, and to calculate the corresponding sleep stages combined with hidden Markov model (HMM) under the no-perception condition. In order to solve the existing problems of sleep staging based on HMM, ensemble empirical mode decomposition (EEMD) was proposed to eliminate the error caused by the individual differences in HRV and then to calculate the corresponding sleep stages. Ten normal subjects of different age and gender without sleep disorders were selected from Guangzhou Institute of Respirator Diseases for heart rate monitoring. Comparing sleep stage results based on HMM to that of polysomnography (PSG), the experimental results validate that the proposed noninvasive monitoring system can capture the sleep stages S1-S4 with an accuracy more than 60%, and performs superior to that of the existing sleep staging scheme based on HMM.
