Nonlinear statistical modeling and model discovery for cardiorespiratory data.

D G Luchinsky; M M Millonas; V N Smelyanskiy; A Pershakova; A Stefanovska; P V E McClintock

doi:10.1103/physreve.72.021905

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Nonlinear statistical modeling and model discovery for cardiorespiratory data.

D G Luchinsky·M M Millonas·V N Smelyanskiy·A Pershakova·A Stefanovska·P V E McClintock

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Abstract

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.

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Algorithms Biological Clocks Blood Pressure Blood Pressure Determination Computer Simulation Diagnosis, Computer-Assisted Heart Rate Humans Models, Biological Models, Statistical Nonlinear Dynamics Oscillometry Pulsatile Flow Respiratory Mechanics Journal Article Research Support, Non-U.S. Gov't

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BibTeX RIS

D G Luchinsky, M M Millonas, V N Smelyanskiy, A Pershakova, A Stefanovska, P V E McClintock. 2005-08-19. Nonlinear statistical modeling and model discovery for cardiorespiratory data.. https://doi.org/10.1103/physreve.72.021905

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