Distinguishing Healthy Subjects from Patients with Congestive Heart Failure Using Scale-Dependent Lyapunov Exponent

Heart rate variability (HRV) time series is highly nonlinear and nonstationary. To effectively characterize its complexity, we employ a newly developed multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE). We derive two readily computable features from the SDLE and show that they can readily distinguish healthy subjects from patients with congestive heart failure (CHF). The same task is evaluated using other complexity measures, including the Hurst parameter, the sample entropy, and the multiscale entropy. It is shown that for the purpose of distinguishing healthy subjects from patients with CHF, the features derived from the SDLE are much more effective than the Hurst parameter, the sample entropy, and the multiscale entropy.