Abstract
The innate variability of the heart rate contains both random and temporally scaled components. The objective of this study was to design a stochastic and mathematically integrative model (SIM) of the control of human heart rate that could replicate the cardiac rhythm during rest and two distinct states of exercise. Heartbeat-to-heartbeat interval (RRI) were recorded from 8 healthy subjects during rest and two exercise levels. Signal memory in RRI sequences was estimated with autocorrelation, and probability density functions (PDFs) were created by fitting polynomial curves to the normalized histograms of the RRI sequences. The SIM generated fictive RRI sequences by randomly selecting RRI values from a PDF and integrating the series using parameters derived from the autocorrelation analysis. Fractal-scaling in the model RRI sequences was quantified with detrended fluctuation analysis. After optimizing the model with the autocorrelation-based and PDF parameters, the SIM was able to produce fictive RRI sequences with significant fractal scaling (statistically assessed with surrogate analysis, p <; 0.001), and with fractal-scaling characteristics similar to the original human data (p = 0.62). Increasing the length of the SIM-generated sequences did not alter the temporal scaling of the model sequences. In conclusion, this research demonstrated a stochastic and integrative model that can replicate the temporally correlated characteristics of the human heart rate.
Published Version
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