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通过符号耦合轨迹定量评估睡眠期间的心血管调节。

Cardiovascular regulation during sleep quantified by symbolic coupling traces.

机构信息

Institute for Applied Computer Science, Forschungszentrum Karlsruhe GmbH (Karlsruhe Research Center), Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany.

出版信息

Chaos. 2010 Dec;20(4):045124. doi: 10.1063/1.3518688.

Abstract

Sleep is a complex regulated process with short periods of wakefulness and different sleep stages. These sleep stages modulate autonomous functions such as blood pressure and heart rate. The method of symbolic coupling traces (SCT) is used to analyze and quantify time-delayed coupling of these measurements during different sleep stages. The symbolic coupling traces, defined as the symmetric and diametric traces of the bivariate word distribution matrix, allow the quantification of time-delayed coupling. In this paper, the method is applied to heart rate and systolic blood pressure time series during different sleep stages for healthy controls as well as for normotensive and hypertensive patients with sleep apneas. Using the SCT, significant different cardiovascular mechanisms not only between the deep sleep and the other sleep stages but also between healthy subjects and patients can be revealed. The SCT method is applied to model systems, compared with established methods, such as cross correlation, mutual information, and cross recurrence analysis and demonstrates its advantages especially for nonstationary physiological data. As a result, SCT proves to be more specific in detecting delays of directional interactions than standard coupling analysis methods and yields additional information which cannot be measured by standard parameters of heart rate and blood pressure variability. The proposed method may help to indicate the pathological changes in cardiovascular regulation and also the effects of continuous positive airway pressure therapy on the cardiovascular system.

摘要

睡眠是一个复杂的调节过程,由短暂的清醒期和不同的睡眠阶段组成。这些睡眠阶段调节自主功能,如血压和心率。符号耦合轨迹(SCT)方法用于分析和量化不同睡眠阶段这些测量值的时滞耦合。符号耦合轨迹定义为双变量单词分布矩阵的对称和对径轨迹,允许量化时滞耦合。在本文中,该方法应用于健康对照组以及伴有睡眠呼吸暂停的正常血压和高血压患者的不同睡眠阶段的心率和收缩压时间序列。使用 SCT,可以揭示不仅在深度睡眠和其他睡眠阶段之间,而且在健康受试者和患者之间存在显著不同的心血管机制。SCT 方法应用于模型系统,与交叉相关、互信息、交叉递归分析等已建立的方法进行比较,并证明其优势,特别是对于非平稳生理数据。结果表明,SCT 在检测方向相互作用的延迟方面比标准耦合分析方法更具特异性,并提供了标准心率和血压变异性参数无法测量的附加信息。该方法可有助于指示心血管调节的病理变化,以及持续气道正压治疗对心血管系统的影响。

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