Characterizing heart rate variability by scale-dependent Lyapunov exponent.
Previous studies on heart rate variability (HRV) using chaos theory, fractal scaling analysis, and many other methods, while fruitful in many aspects, have produced much confusion in the literature. Especially the issue of whether normal HRV is chaotic or stochastic remains highly controversial. Here, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize HRV. SDLE has been shown to readily characterize major models of complex time series including deterministic chaos, noisy chaos, stochastic oscillations, random 1/f processes, random Levy processes, and complex time series with multiple scaling behaviors. Here we use SDLE to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure, 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.
Hu
Jing
J
PMB Intelligence LLC, P.O. Box 2077, West Lafayette, Indiana 47996, USA.
Gao
Jianbo
J
Tung
Wen-wen
WW
D001281:Atrial Fibrillation / Q000503:physiopathology; D017709:Fractals; D006333:Heart Failure / Q000503:physiopathology; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D017711:Nonlinear Dynamics; D012016:Reference Values; D013269:Stochastic Processes