Key result
Nonlinear heart-rate dynamics methods offer expanding utility for assessing healthy and pathological cardiac states.
Why the study?
Although mathematical methods to evaluate heart-rate variability have expanded knowledge of cardiovascular dynamics, they remain far from clinical practice.
This review clarifies the mathematical foundations and clinical applications of nonlinear heart-rate variability methods, providing a resource to help researchers correctly apply these complex techniques to physiological data.
May guide researchers applying nonlinear HRV methods; leaves open routine clinical adoption.
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.
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Henriques et al. (2020) conducted a review in Heart-rate variability. Nonlinear methods (Poincaré plot, recurrence plot, fractal dimension, DFA, etc.) was evaluated. Nonlinear methods based on chaos, fractality, and complexity, such as Poincaré plots and sample entropy, are increasingly applied to assess heart-rate dynamics in healthy and pathological conditions.
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