摘要
睡眠状况是评价人体健康状态的重要指标。本文提出一种基于枕下式的无扰睡眠监测系统,通过无扰获取的心率信号测算心率变异性(HRV),并结合隐马尔可夫模型(HMM),在对用户无扰无接触的环境下求解睡眠分期。针对现有HMM睡眠分期存在的问题,提出采用集合经验模态分解(EEMD)消除HRV个体差异导致的分期误差,再求解相应的睡眠分期。试验选取广州医学院呼吸疾病研究所10例不同年龄及性别的无睡眠障碍的院内正常受试者,并与多导睡眠图(PSG)睡眠分期结果相比较。研究结果证明本文所提无扰式睡眠监测方案可实现S1~S4睡眠分期,正确率超过60%,且性能优于现有HMM睡眠分期方案。
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.
作者
李翔
刘勇
陈澎彬
吴洁伟
张涵
LI Xiang;LIU Yong;CHEN Pengbin;WU Jiewei;ZHANG Han(Institute of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, P.R.China;Guangzhou SENVIV Technology Co. Ltd, Guangzhou 510006, P.R.China;Guangdong Provincial Engineering Research Center for Cardiovascular Individual Medicine ＆ Big Data, Guangzhou 510006, P.R.China)
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2018年第2期280-289,共10页
Journal of Biomedical Engineering
基金
国家自然科学基金(61471176
61471175)
华南师范大学研究生创新计划项目(20161kxm47)
广东省科技计划项目(2017A010101015
2017B030308009
2017KZ010101)
广东省特支计划(2016TQ03X100)
广东省优秀青年教师培养计划(YQ2015046)
广州市珠江科技新星专项(201610010199)
广东省大学生科技攀登计划(pdjh2017a0127)
关键词
睡眠分期
心率变异性
隐马尔可夫模型
集合经验模态分解
sleep stages
heart rate variability
hidden Markov model
ensemble empirical mode decomposition
