Modeling heart rate variability including the effect of sleep stages

Abstract

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.

Multifractal‐Multiscale Analysis of Cardiovascular Signals: A DFA‐Based Characterization of Blood Pressure and Heart‐Rate Complexity by Gender

“…In this regard, recently Gierałtowski et al combined both the approaches: they proposed a multifractal and multiscale method for the DFA of heart-rate variability, exploiting the possibility of adapting the multifractal DFA algorithm in order to provide estimates separately at different scales [12]. This method was recently applied for modeling heart-rate variability during sleep and bloodpressure variability [17,18]. In the present study, we followed a similar approach, introducing, however, important variants.…”

A Fast DFA Algorithm for Multifractal Multiscale Analysis of Physiological Time Series

“…In this field, the evaluation of the self-similarity exponents as a continuous function of the scale, α( n ), proved useful for associating a short-term crossover to the dynamics of removal of noradrenaline released by the sympathetic nerve endings (Castiglioni, 2011), for quantifying alterations during sleep at high-altitude (Castiglioni et al, 2011b) and for evaluating clinical conditions like congestive heart failure (Bojorges-Valdez et al, 2007) or spinal lesions (Castiglioni and Merati, 2017). When the self-similarity exponents were estimated as a multifractal multiscale surface of the moment q and scale n , α( q,n ), they provided information on the autonomic development from fetal heart rate recordings (Gieraltowski et al, 2013), on differences between the dynamics of heart rate and other cardiovascular variables (Castiglioni et al, 2018) and helped modeling the heart rate dynamics during sleep (Solinski et al, 2016).…”

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

“…These anti-correlations were strong during deep sleep/slow-wave-sleep, weaker during light sleep, and even weaker during REM sleep, a finding very useful for modeling transient correlations in heartbeat dynamics during sleep considering the sleep stages (Kantelhardt et al, 2003). This has very recently been used as a starting point for a more sophisticated model (Soliński et al, 2016) with program code available on PHYSIONET (Goldberger et al, 2000). …”