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Arterial baroreflex influence on heart rate variability: A mathematical model-based analysis

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Abstract

The influence of the arterial baroreflex on the heart rate variability is analysed by using a mathematical model of heart rate baroreceptor control. The basic mechanisms of the model, sufficient to elicit heart rate variability include: systemic circulation, a non-pulsatile cardiac pump and nonlinear negative feedback simulating arterial baroreflex closed-loop control of the heart rate (−3bpm/mmHg as maximum reflex sensitivity). The latter reproduces, through two distinct delayed branches (0.8 and 2.8 s), the short-term autonomic control effected respectively by sympathetic and parasympathetic divisions on the sinus node. By means of this model, two distinct self-sustained oscillatory components with incommensurate frequencies (0.1 and 0.26 Hz) are reproduced. Frequencies of these two oscillatory components closely agree with the main heart rate rhythms in humans (0.09±0.01 Hz and 0.26±0.01 Hz). When sympathetic-mediated regulation prevails over parasympathetic activity, simulated heart rate oscillation is characterised by a low frequency (∼0.1 Hz). On the other hand, a high-frequency oscillatory component (∼0.26 Hz) appears when enhanced vagal activation or partial inhibition of the sympathetic control is simulated. When both autonomic divisions are operative, both low- and high-frequency components are present and the heart rate oscillates quasi-periodically. This variability in heart rate at different frequencies is reproduced without including outside perturbations and is due to the nonlinear delayed structure of the closed-loop control. Bifurcation theory of nonlinear system is used to explain the high sensitivity of the heart rate oscillatory pattern to model parameter changes.

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  1. Laboratory of Biomedical Engineering, Department of Electronics, Computer Science and Systems, University of Bologna, Italy

    S. Cavalcanti

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Cavalcanti, S. Arterial baroreflex influence on heart rate variability: A mathematical model-based analysis. Med. Biol. Eng. Comput. 38, 189–197 (2000). https://doi.org/10.1007/BF02344775

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