A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability
A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability
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DOI：
10.1152/ajpheart.00482.2003
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发表时间：
2005-01-01
影响因子：
4.8
通讯作者：
Brown, EN
中科院分区：
文献类型：
--
作者：
Barbieri, R;Matten, EC;Brown, EN
心率是一种生命体征，而心率变异性是自主神经系统对心血管调节的重要定量指标。虽然设计计算心率和评估心率变异性的算法是一个活跃的研究领域，但没有一种方法考虑到人类心跳的自然点过程结构，也没有一种方法给出心率变异性的瞬时估计。我们将心跳间隔的随机结构建模为历史相关的逆高斯过程，并从中得出一个明确的概率密度，该概率密度给出了心率和心率变异性的新定义：瞬时R-R间隔和心率标准差。利用局部极大似然法估计逆高斯模型的时变参数，并根据时间重标定理利用Kolmogorov-Smirnov检验评估模型的拟合优度。我们通过对10名健康受试者进行倾斜实验的人类心跳间隔的分析来说明我们的新定义。虽然一些研究已经确定了人类心跳间隔的确定性、非线性动态特征，但我们的分析表明，在静止和极端生理条件下，这些序列的高度准确描述可能是由一个基本的、基于生理学的随机模型给出的。
Heart rate is a vital sign, whereas heart rate variability is an important quantitative measure of cardiovascular regulation by the autonomic nervous system. Although the design of algorithms to compute heart rate and assess heart rate variability is an active area of research, none of the approaches considers the natural point-process structure of human heartbeats, and none gives instantaneous estimates of heart rate variability. We model the stochastic structure of heartbeat intervals as a history-dependent inverse Gaussian process and derive from it an explicit probability density that gives new definitions of heart rate and heart rate variability: instantaneous R-R interval and heart rate standard deviations. We estimate the time-varying parameters of the inverse Gaussian model by local maximum likelihood and assess model goodness-of-fit by Kolmogorov-Smirnov tests based on the time-rescaling theorem. We illustrate our new definitions in an analysis of human heartbeat intervals from 10 healthy subjects undergoing a tilt-table experiment. Although several studies have identified deterministic, nonlinear dynamical features in human heartbeat intervals, our analysis shows that a highly accurate description of these series at rest and in extreme physiological conditions may be given by an elementary, physiologically based, stochastic model.