睡眠呼吸暂停病人HRV和PRV的相关性研究

发布时间：2018-04-24 23:17

本文选题：心率变异性 + 脉率变异性　；参考：《山东大学》2017年硕士论文

【摘要】：随着现代生活节奏不断加快,各种睡眠障碍疾病层出不穷,渐渐成为一个突出的健康问题,受到人们的广泛关注。据世界卫生组织流行病学调查,在世界范围内约1/3的人患有睡眠障碍,我国有各类睡眠障碍的人更是高达38.2%。目前我国已明确与睡眠障碍相关的疾病多达八十余种,其中睡眠呼吸暂停综合征约占60～70%。20世纪70年代起,美国斯坦福大学成立了专门诊治睡眠疾患的睡眠中心,由此,睡眠呼吸暂停综合征成为一个独立疾病。根据2007年美国睡眠医学会制定的新的睡眠分期准则,将睡眠分为入睡期,浅睡期,深睡期和快速眼动期。每个睡眠阶段都有其独特的脑电波信号和不同的生理状态反应,研究每个睡眠阶段的特点和他们之间的差异性及相关性是非常有意义的。在过去的几十年里,从心电图中提取的心率变异性(HRV)已经成为临床诊断的一个非常有用的工具。心率变异性对心脏自主神经功能的评估具有重要意义,被作为反映交感迷走神经的交互作用的指标。但是心电图至少需要三个导联的电极,而脉搏检测信号通常只需要在手指或耳垂上安装一个传感器,具有成本效益性和直接使用性。在过去的研究中,就从脉搏中提取的脉率变异性(PRV)能否替代心率变异性的问题仍存在争议。本文针对睡眠呼吸暂停病人,在各个睡眠阶段分别提取脉率变异性和心率变异性,利用统计学的原理进行对比分析,论证了 PRV能够代替HRV。主要工作包括以下几个方面:1)睡眠阶段准确的分类。睡眠阶段分为快速眼动期(REM)和非快速眼动期(NREM),其中非快速眼动期又分为入睡期(N1)、浅睡期(N2)、深睡期(N3)。在不同的睡眠阶段,分别选取出包含睡眠呼吸暂停事件的心电信号和脉搏信号片段。2)HRV和PRV的构建。确定心电信号和脉搏信号的峰值点,其中心电信号提取QRS波群的R波峰值点,脉搏信号本文也选取其周期内的峰值,分别形成RR间期和PP间期时间序列并保存以供后续研究分析。3)HRV和PRV的统计性分析。针对于RJR间期和PP间期时间序列来提取HRV和PRV指标,运用统计学的原理,对HRV和PRV指标分别从时域、频域、非线性三个领域在不同睡眠阶段进行差异性和相关性分析,并且研究HRV和PRV指标随着睡眠的加深的变化趋势。
[Abstract]:With the accelerating pace of modern life, various sleep disorders emerge in endlessly, and gradually become a prominent health problem, which has been widely concerned by people. According to the World Health Organization epidemiological survey, about a third of the world's people suffer from sleep disorders, and the number of people with all kinds of sleep disorders in China is as high as 38.2%. At present, more than 80 kinds of diseases related to sleep disorders have been identified in China, of which sleep apnea syndrome accounts for about 6070%. Since the 1970s, Stanford University in the United States has established a sleep center dedicated to the diagnosis and treatment of sleep disorders. Sleep apnea syndrome has become an independent disease. According to the new sleep staging guidelines formulated by the American Academy of Sleep Medicine in 2007, sleep was divided into three periods: sleep period, shallow sleep period, deep sleep period and rapid eye movement period. Each sleep stage has its own unique brain wave signal and different physiological state response. It is very meaningful to study the characteristics of each sleep stage and their differences and correlations. Heart rate variability (HRV) extracted from electrocardiogram (ECG) has become a very useful tool for clinical diagnosis in the past few decades. Heart rate variability (HRV) plays an important role in the evaluation of cardiac autonomic nervous function and is used as an indicator of sympathetic vagus interaction. But electrocardiograms require at least three leads of electrodes, and pulse signals usually require only one sensor on the finger or earlobe, which is cost-effective and directly useful. In previous studies, whether pulse rate variability (PRV) extracted from pulse can replace heart rate variability (HRV) remains controversial. In this paper, pulse rate variability and heart rate variability were extracted from patients with sleep apnea in different sleep stages. The statistical principle was used to compare and analyze, and it was demonstrated that PRV could replace HRV. The main work includes the following aspects: 1) accurate classification of sleep stages. The sleep stage was divided into rapid eye movement (REM) and non-rapid eye movement (NREMN), among which the non-REM stage was divided into sleep phase (N1), shallow sleep stage (N2) and deep sleep stage (N3). At different sleep stages, ECG and pulse signal fragments containing sleep apnea events. 2HRV and PRV were constructed respectively. The peak point of ECG signal and pulse signal is determined, and the peak point of R wave of QRS wave group is extracted by its central signal. The peak value of pulse signal is also selected in this paper. RR interval and PP interval time series were formed and preserved for statistical analysis of HRV and PRV. Aiming at extracting HRV and PRV indexes from RJR interval and PP interval time series, the differences and correlations of HRV and PRV indexes in different sleep stages were analyzed from time domain, frequency domain and nonlinearity by using the principle of statistics. The changes of HRV and PRV indexes with the deepening of sleep were studied.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7;R766

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