Key result
A stochastic model of nighttime heart rate variability incorporating sleep architecture generated synthetic HRV signals with properties very similar to real polysomnographic data.
A proposed stochastic model incorporating sleep architecture successfully simulates human nighttime heart rate variability with properties similar to real physiological data.
Enables synthetic nighttime HRV datasets for research; leaves open clinical validation and utility.
We propose a model for heart rate variability (HRV) of a healthy individual during sleep with the assumption that the heart rate variability is predominantly a random process. Autonomic nervous system activity has different properties during different sleep stages, and this affects many physiological systems including the cardiovascular system. Different properties of HRV can be observed during each particular sleep stage. We believe that taking into account the sleep architecture is crucial for modeling the human nighttime HRV. The stochastic model of HRV introduced by Kantelhardt et al. was used as the initial starting point. We studied the statistical properties of sleep in healthy adults, analyzing 30 polysomnographic recordings, which provided realistic information about sleep architecture. Next, we generated synthetic hypnograms and included them in the modeling of nighttime RR interval series. The results of standard HRV linear analysis and of nonlinear analysis (Shannon entropy, Poincaré plots, and multiscale multifractal analysis) show that-in comparison with real data-the HRV signals obtained from our model have very similar properties, in particular including the multifractal characteristics at different time scales. The model described in this paper is discussed in the context of normal sleep. However, its construction is such that it should allow to model heart rate variability in sleep disorders. This possibility is briefly discussed.
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Soliński et al. (2016) studied Healthy adults (sleep) (n=30). Stochastic model of HRV incorporating sleep architecture vs. Real polysomnographic data was evaluated on HRV signal properties (linear and nonlinear analysis). A stochastic model of nighttime heart rate variability incorporating sleep architecture generated synthetic HRV signals with properties very similar to real polysomnographic data.
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