Modeling of human heart rate variability enhanced using stochastic sleep architecture properties

Human sleep consists of four characteristic phases: light (L), deep (D), REM sleep and almost-awake state (W) with additional arousal episodes (Exc). All of these elements create a nontrivial, complex structure, the statistical properties of which were studied here carefully. We observed a different behavior of heart rate variability for each phase. Thus, we should take these specific properties of sleep architecture into consideration while modeling heart rate variability.We analyzed 34 simultaneous heart rate variability and 30 EEG nighttime recordings from healthy adults. EEG provides accurate information about sleep phases. The main idea behind our sleep architecture reconstruction is to consider two properties: probabilities of transitions between all possible pairs of phases and probability distribution of phase durations. We calculated the probabilities of transition between each pair of phases and we aggregated them into two probability matrices (separately for each half of the sleep period because the character of the inter-phase transitions is diferent in early and late sleep). We found also that the probability distribution of L, D and REM sleep duration are described by a gamma distribution and that of the W phase - by a Pareto distribution. To generate the RR intervals for every sleep phase, we use the model described in [1j. We consider three variants: (a) periodic sleep cycles with the sequences of phases: L, D, REM, W in each cycle, (b) a randomized distribution of phases, (c) the architecture based on our model. The results show that variant (c) gives 50% of the time series indistinguishable from real data using all standard linear and nonlinear HRV assessment methods while for variants (a) and (b) we obtain 41% and 3% accordingly.