Key result
A nonstationary Langevin equation model approximated cardiological data and explained effects observed in the reconstruction of the deterministic part of the Langevin equation for heart rate time series.
The nonstationary Langevin equation can be used to model and explain statistical properties observed in heart rate time series.
Offers a modeling framework for heart rate dynamics; leaves open clinical validation before any diagnostic use.
Using the Langevin equation we develop the model of a stochastic process subject to a given time-dependent regulatory mechanism. The effects of this nonstationarity on the statistical properties of the time series, i.e., on global and conditional probability densities and on the moments of the distribution, are derived. Application of these results on simple model trends allows one to approximate cardiological data and thus to explain effects recently observed in the reconstruction of the deterministic part of the Langevin equation for time series of heart rate.
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Kirchner et al. (2007) studied Heart rate time series. Nonstationary Langevin equation model was evaluated on Statistical properties of the time series (global and conditional probability densities and moments of the distribution). A nonstationary Langevin equation model approximated cardiological data and explained effects observed in the reconstruction of the deterministic part of the Langevin equation for heart rate time series.
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