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Nonlinear statistical modeling and model discovery for cardiorespiratory data.

Luchinsky, Dmitry G. and Millonas, M. M. and Smelyanskiy, V. N. and Pershakova, A. and Stefanovska, Aneta and McClintock, Peter V. E. (2005) Nonlinear statistical modeling and model discovery for cardiorespiratory data. Physical Review E, 72 (2). 021905. ISSN 1539-3755

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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.

Item Type: Article
Journal or Publication Title: Physical Review E
Uncontrolled Keywords: cardiovascular system ; pneumodynamics ; statistical analysis ; stochastic processes ; nonlinear dynamical systems
Subjects: Q Science > QC Physics
Departments: Faculty of Science and Technology > Physics
ID Code: 9409
Deposited By: Ms Margaret Calder
Deposited On: 06 Jun 2008 16:48
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 18:36
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/9409

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