Nonlinear dynamic modeling provides additional insights into heart rate variability beyond traditional spectral analysis by capturing deterministic chaos properties.
Results which illustrate a kind of passage from heart rate variability (HRV) spectra to parameters which measure the deterministic chaos properties of long-term RR series are presented. Nonlinear dynamic modeling may account for some information which cannot be explained by the traditional spectral analysis. Some algorithms which enhance statical and dynamical characteristics of the system behavior under a state-phase form are described. A continuity from the spectral parameters which describe physiological rhythms to other parameters which account for statical and dynamical characteristics of a strange attractor which is formed by the trajectories of a state-phase vector which defines the system is discussed.>
Cerutti et al. (Mon,) studied this question.
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