PMB Intelligence LLC, P.O. Box 2077, West Lafayette, IN 47996, USA.
Ann Biomed Eng. 2010 Mar;38(3):854-64. doi: 10.1007/s10439-009-9863-2. Epub 2009 Dec 12.
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.
心率变异性（HRV）是心血管功能的一个重要动力学变量。已经有许多努力来确定 HRV 动力学是混沌的还是随机的，以及某些复杂性度量是否能够区分健康受试者和患有某些心脏病的患者。在这项研究中，我们采用了一种新的多尺度复杂性度量，即尺度相关的 Lyapunov 指数（SDLE），来描述健康、充血性心力衰竭（CHF）和心房颤动受试者的 HRV 中非线性、混沌和随机动力学的相对重要性。我们表明，尽管这三种类型的 HRV 数据主要是随机的，但这三组之间的随机性是不同的。此外，我们表明，对于区分健康受试者和 CHF 患者的目的，SDLE 衍生的特征比其他复杂性度量（如 Hurst 参数、样本熵和多尺度熵）更有效。
