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Nonlinear statistical modeling and model discovery for cardiorespiratory data.

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<mark>Journal publication date</mark>08/2005
<mark>Journal</mark>Physical Review E
Issue number2
Pages (from-to)021905
<mark>Original language</mark>English


We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.