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QT interval (QT) variability analysis provides pathophysiological and prognostic information utilized in cardiac and non-cardiac diseases, complementary to those obtained from the analysis of heart period (HP) variability. An increased QT variability has been associated to a higher risk for cardiac events and poorest prognosis. Autonomic cardiovascular adaptation to internal and external challenges, such those occurring in athletes exposed to high levels of physical stress and in ageing could also be deepen by analyzing QT variability, searching for early prognostic signatures. The aim of the study was to analyze the QT variability and cardiac control complexity in a group of middle-aged half-marathon runners at baseline (B) and at a 10-year follow-up (FU). We found that the overall QT variability decreased at FU, despite the inescapable increase in age (52.3 ± 8.0 years at FU). This change was accompanied by an increase of the HP variability complexity without changes of the QT variability complexity. Of notice, over the years, the group of athletes maintained their regular physical activity by switching to a moderate intensity rather than strenuous. In conclusion, regular and moderate exercise over the years was beneficial for this group of athletes, as reflected by the decreased overall QT variability that is known to be associated to lower cardiovascular risk. The concomitant enhanced cardiac control complexity also suggests a trend opposite to what usually occurs with ageing, resulting in a more flexible cardiac control, typical of younger people. [ABSTRACT FROM AUTHOR]

Objective: Traditional definition of sample entropy (SampEn), here referred to as global SampEn (GSampEn), provides a conditional entropy estimate that blurs the local statistical properties of the time series. We hypothesized that a local version of SampEn (LSampEn) might be more powerful in the presence of determinism than GSampEn. Methods: LSampEn was computed by calculating the probability of the current sample conditioned on each reference pattern and averaging it over all reference patterns. The improved ability of LSampEn compared to GSampEn was demonstrated by simulating deterministic periodic, deterministic chaotic, and linear stochastic dynamics corrupted by additive noise and over real cardiovascular variability series recorded from 16 healthy subjects (max–min age range: 22–58 years) during incremental bicycle ergometer exercise. Results: We found that: i) LSampEn is more robust in describing deterministic periodic or nonlinear features in the presence of additive noise than GSampEn, ii) in association with a surrogate approach, LSampEn is more powerful in detecting nonlinear dynamics than GSampEn, iii) LSampEn and GSampEn are equivalent in the presence of stochastic linear dynamics, and iv) only LSampEn can detect the decrease of complexity of heart period variability during bicycle exercise being a likely hallmark of sympathetic activation. Conclusion: LSampEn preserves the GSampEn capability in characterizing the complexity of short sequences but improves its reliability in the presence of deterministic patterns featuring sharp state transitions and nonlinear dynamics. Significance: Variations of complexity can be measured with a greater statistical power over short series using LSampEn, especially when nonlinear features are present. [ABSTRACT FROM AUTHOR]