Abstract
The control mechanisms and implications of heart rate variability (HRV) under the sympathetic (SNS) and parasympathetic nervous system (PNS) modulation remain poorly understood. Here, we establish the HR model/HRV responder using a nonlinear process derived from Newton’s second law in stochastic self-restoring systems through dynamic analysis of physiological properties. We conduct model validation by testing, predictions, simulations, and sensitivity and time-scale analysis. We confirm that the outputs of the HRV responder can be accepted as the real data-generating process. Empirical studies show that the dynamic control mechanism of heart rate is a stable fixed point, rather than a strange attractor or transitions between a fixed point and a limit cycle; HR slope (amplitude) may depend on the ratio of cardiac disturbance or metabolic demand mean (standard deviation) to myocardial electrical resistance (PNS-SNS activity). For example, when metabolic demands remain unchanged, HR amplitude depends on PNS to SNS activity; when autonomic activity remains unchanged, HR amplitude during resting reflects basal metabolism. HR parameter alterations suggest that age-related decreased HRV, ultrareduced HRV in heart failure, and ultraelevated HRV in ST segment alterations refer to age-related decreased basal metabolism, impaired myocardial metabolism, and SNS hyperactivity triggered by myocardial ischemia, respectively.
Highlights
The control mechanisms and implications of heart rate variability (HRV) under the sympathetic (SNS) and parasympathetic nervous system (PNS) modulation remain poorly understood
We discovered that the noise-driven homeostatic HRV responder failed to simulate HRV situated at the edge of stability in a subset of ST, SCD, and CHF groups
We established the nonlinear autoregressive integrated (NLARI)-HR model and HRV responder as the heartbeat data generative processes driven by myocardial noise or stimulus using the NLARI process based on dynamic analysis
Summary
The control mechanisms and implications of heart rate variability (HRV) under the sympathetic (SNS) and parasympathetic nervous system (PNS) modulation remain poorly understood. We establish the HR model/HRV responder using a nonlinear process derived from Newton’s second law in stochastic self-restoring systems through dynamic analysis of physiological properties. The use of a resampling procedure along with the size-related correlations of the nonlinear estimator area[1] of approximate entropy provides an effective method to discern different generating processes underlying heart rate time series[31]. The key point to achieving the aim is whether the HR model established can pass the validation, that is, whether the outputs of the HR model is acceptable with respect to the real heart rate data-generating process This includes considering whether the model has the capability to properly capture the main characteristics of heart rate dynamics. This method was applied to detect valuable information from heart rate data and to interpret unsolved problems, which in turn would again validate the HR model
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