Despite the widespread diffusion of nonlinear methods for heart rate variability (HRV) analysis, the presence and the extent to which nonlinear
dynamics contribute to short-term HRV are still controversial. This work aims at testing the hypothesis that different types of nonlinearity can be
observed in HRV depending on the method adopted and on the physiopathological state. Two entropy-based measures of time series complexity
(normalized complexity index, NCI) and regularity (information storage, IS), and a measure quantifying deviations 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 the resting supine position and in the upright position reached through head-up tilt. The
method of surrogate data is employed to detect the presence and quantify the contribution of nonlinear dynamics to HRV. We find that the
three measures differ both in their variations across groups and conditions and in the percentage and strength of nonlinear HRV dynamics.
NCI and IS displayed opposite variations, suggesting more complex dynamics in O and MI compared to Y and less complex dynamics during
tilt. The strength of nonlinear dynamics is reduced by tilt using all measures in Y, while only GLC detects a significant strengthening of such
dynamics in MI. A large percentage of detected nonlinear dynamics is revealed only by the IS measure in the Y group at rest, with a decrease
in O and MI and during T, while NCI and GLC detect lower percentages in all groups and conditions. While these results suggest that distinct
dynamic structures may lie beneath short-term HRV in different physiological states and pathological conditions, the strong dependence on
the measure adopted and on their implementation suggests that physiological interpretations should be provided with caution.
