Multiscale analysis of heart rate variability: a comparison of different complexity measures
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.
Date of this Version
Hu, Jing; Gao, Jianbo; Tung, Wen-wen; and Cao, Yinhe, "Multiscale analysis of heart rate variability: a comparison of different complexity measures" (2010). Department of Earth, Atmospheric, and Planetary Sciences Faculty Publications. Paper 119.
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