Heart rate dynamics in short-time series can be modeled as a sum of linear and nonlinear processes, with stochastic processes dominating at rest and decreasing during mental stress or fatigue.
May aid HRV stress assessment but should not change practice; leaves open validation of rest-state stochastic dominance in clinical cohorts.
The relationship of the slope of the heart rate graph regression curve (b 1) with periodic (linear) and nonlinear heart rate dynamics has been studied in stationary short-time series (256 points). For estimating nonlinear dynamics, a parameter derived from correlation dimension has been used, which has made it possible to estimate chaotic processes in short-time series. According to the results of the study, the heart rate dynamics in short-time series may be represented as a sum of linear (periodic) and nonlinear (stochastic) processes. The relationships of b 1 with both the linear and the nonlinear heart rate dynamics have been demonstrated. Equations for calculating the absolute and relative (to the periodic oscillation amplitude) noises in the heart rate dynamics in short-time series are proposed. Stochastic nonlinear dynamics in different physiological states of humans have been compared. It has been found that the increase in the relative noise intensity in the heart rate dynamics with an increase in respiration rate is determined not only by the decrease in the amplitude of respiratory waves, but also by an increase in the amplitude of the noise itself. The absolute noise intensity is decreased in the states of neurotic excitement, fatigue, and, especially, mental stress. In the state of rest, nonlinear (stochastic) processes dominate over linear (periodic) ones.
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V. A. Mashin (2006) studied this question.
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