Abstract
The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.
Highlights
The term chaos, in science, refers to a mathematical approach dealing with systems that are fully describable but which generate randomly appearing outputs under certain conditions [1]
The authors found that (1) all nonlinear indices present a day–night variation except for the correlation dimension (CD) in the female population; (2) gender-related differences only existed in approximate entropy (ApEn), detrended fluctuation analysis (DFA), and the Lyapunov exponent (LE); (3) all nonlinear indexes were significantly correlated with age during daytime hours
This review paper reviews most common nonlinear methods applied to heart-rate time series
Summary
The term chaos, in science, refers to a mathematical approach dealing with systems that are fully describable but which generate randomly appearing outputs under certain conditions [1]. Chaos theory deals with patterns in the time evolution of a nonlinear system that is sensitive to initial conditions. A fractal is an object composed of subunits that resemble the larger-scale structure [2]. This property of self-similarity (or scale invariance) means that the details of the structures are similar but not necessarily identical when zooming at different resolutions. The definition of fractal goes beyond self-similarity per se to include the idea of a detailed pattern repeating itself at increasingly small scales. The self-similarity of the system’s fluctuations can be observed when a signal is analyzed over different time scales
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