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Despite the widespread diffusion of nonlinear methods for heart ratevariability (HRV) analysis, the presence and the extent to which nonlineardynamics contribute to short-term HRV is still controversial. This work aims attesting the hypothesis that different types of nonlinearity can be observed inHRV depending on the method adopted and on the physiopathological state. Twoentropy-based measures of time series complexity (normalized complexity index,NCI) and regularity (information storage, IS), and a measure quantifyingdeviations from linear correlations in a time series (Gaussian linear contrast,GLC), are applied to short HRV recordings obtained in young (Y) and old (O)healthy subjects and in myocardial infarction (MI) patients monitored in theresting supine position and in the upright position reached through head-uptilt. The method of surrogate data is employed to detect the presence of andquantify the contribution of nonlinear dynamics to HRV. We find that the threemeasures differ both in their variations across groups and conditions and inthe number and strength of nonlinear HRV dynamics detected: at rest, IS revealsa significantly lower number of nonlinear dynamics in Y, whereas during tiltGLC reveals significantly stronger nonlinear HRV dynamics in MI; in thetransition from rest to tilt, all measures detect a significant weakening ofnonlinear HRV dynamics in Y, while only GLC detects a significant strengtheningof such dynamics in MI. These results suggest that distinct dynamic structures,detected with different sensitivity by nonlinear measures, lie beneathshort-term HRV in different physiological states and pathological conditions.

Comparison of Methods for the Assessment of Nonlinearity in Short-Term Heart Rate Variability under different Physiopathological States