Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures

Abstract

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.

On the dynamics of ocean ambient noise: Two decades later

“…13 Since then, SDLE is used in various fields of research for characterizing the dynamic complexity of the time series signals. [47][48][49][50][51][52][53] SDLE can efficiently characterize the embedded dynamics in complex time series by identifying chaos, noisy chaos, stochastic oscillations, random 1/f process, random levy process, and complex time series with multiple scaling behaviors. Moreover, it is robust to nonstationarity.…”

A Refined Multiscale Self-Entropy Approach for the Assessment of Cardiac Control Complexity: Application to Long QT Syndrome Type 1 Patients

“…While the CE is more widely utilized as a measure of complexity of a series [1][2][3], sE is traditionally exploited to assess regularity and predictability of a process [8] or information stored in it [7,9]. CE depends on time scales and this dependence, usually assessed via multiscale conditional entropy (MSCE) approach [11] or via its refinement referred to as refined MSCE (RMSCE) [12], provides relevant information about cardiovascular regulation because it allows the focalization of cardiac control mechanisms acting over an assigned time scale [11][12][13][14][15][16][17]. While the importance of monitoring the CE as a function of the time scale via MSCE or RMSCE is indubitable, it is unclear whether sE is worth to be monitored as a function of the time scale.…”

Detecting low-dimensional chaos by the “noise titration” technique: Possible problems and remedies

“…Two major difficulties for solving this issue are (i) chaos can be induced by noise [1][2][3][4][5][6][7][8], and (ii) standard Brownian motions may have a deterministic origin [8,9]. Although many methods have been proposed to distinguish chaos from noise [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], it is generally difficult to fully sort out the capabilities and limitations of a particular method.…”

“…The third is the scale-dependent Lyapunov exponent (SDLE) [9][10][11][12]. Conceptually, FSLE and SDLE are closely related.…”