生物医学工程学杂志

生物医学工程学杂志

基于隐马尔可夫模型的枕下无扰式新型睡眠监测方案

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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.

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

Key words: sleep stages; heart rate variability; hidden Markov model; ensemble empirical mode decomposition

引用本文: 李翔, 刘勇, 陈澎彬, 吴洁伟, 张涵. 基于隐马尔可夫模型的枕下无扰式新型睡眠监测方案. 生物医学工程学杂志, 2018, 35(2): 280-289. doi: 10.7507/1001-5515.201703059 复制

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