Abstract
A physiologically motivated, dynamical model of cardiovascular autonomic regulation is shown to be capable of generating long-range correlated and multifractal heart rate. Virtual disease simulations are carried out systematically to account for the disease-induced relative dysfunction of the parasympathetic and the sympathetic branches of the autonomic control. Statistical agreement of the simulation results with those of real life data is reached, suggesting the possible use of the model as a state-of-the-art basis for further understanding of the physiological correlates of complex heart rate dynamics.
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