Regularity patterns in heart rate variability signal: the approximate entropy approach

Recent approaches to the study of nonlinearity in biological systems have found a powerful tool in the approximate entropy (ApEn) estimation. ApEn is a family of statistic indices that measures different degrees of regularity in time series without any a priori hypothesis about the system structure generating them. In this paper we analyze its ability to distinguish, in the short period, different physiological conditions in which the cardiovascular control system can influence the heart rate variability signal (HRV). At the same time we show results confirming a significant separation in myocardial infarction populations on the basis of the ApEn index. The experimental work also discusses the choice of parameters for a correct ApEn index estimation and its link with the neural mechanisms controlling HRV signal.