Characterizing heart rate variability by scale-dependent Lyapunov exponent

Abstract

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.

Date of this Version

2009

DOI

http://dx.doi.org/10.1063/1.3152007

Volume

19

Issue

2

Pages

028506-028513

Link Out to Full Text

http://chaos.aip.org/resource/1/chaoeh/v19/i2/p028506_s1

Link to Full Text

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