Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures
Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures
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DOI：
10.1007/s10439-009-9863-2
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发表时间：
2010-03-01
影响因子：
3.8
通讯作者：
Cao, Yinhe
中科院分区：
文献类型：
--
作者：
Hu, Jing;Gao, Jianbo;Cao, Yinhe
心率变异性（HRV）是心血管功能的重要动力学变量。已经有许多努力来确定HRV动力学是混沌的还是随机的，以及某些复杂性测量是否能够区分健康受试者与患有某些心脏疾病的患者。在这项研究中，我们采用了一种新的多尺度复杂性的措施，尺度依赖的李雅普诺夫指数（SDLE），非线性，混沌和随机动力学的健康，充血性心力衰竭（CHF），心房颤动的HRV的相对重要性进行表征。我们发现，虽然所有这三种类型的HRV数据大多是随机的，随机性是不同的三个群体。此外，我们表明，为了区分健康受试者与CHF患者，来自SDLE的功能比其他复杂性措施，如赫斯特参数，样本熵，和多尺度熵更有效。
Heart rate variability (HRV) is an important dynamical variable of the cardiovascular function. There have been numerous efforts to determine whether HRV dynamics are chaotic or random, and whether certain complexity measures are capable of distinguishing healthy subjects from patients with certain cardiac disease. In this study, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure (CHF), and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups. Furthermore, we show that for the purpose of distinguishing healthy subjects from patients with CHF, features derived from SDLE are more effective than other complexity measures such as the Hurst parameter, the sample entropy, and the multiscale entropy.