Nonlinear statistical modeling and model discovery for cardiorespiratory data

D. G. Luchinsky, M. M. Millonas, V. N. Smelyanskiy, A. Pershakova, A. Stefanovska, and P. V. E. McClintock
Phys. Rev. E 72, 021905 – Published 19 August 2005

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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  • Received 12 April 2005

DOI:https://doi.org/10.1103/PhysRevE.72.021905

©2005 American Physical Society

Authors & Affiliations

D. G. Luchinsky1,2,3, M. M. Millonas2, V. N. Smelyanskiy2, A. Pershakova3, A. Stefanovska3,4, and P. V. E. McClintock3

  • 1Newstead Mission Critical Technologies, Inc., 9100 Wilshire Boulevard, Suite 540, East Beverly Hills, California 90212-3437, USA
  • 2NASA Ames Research Center, Mail Stop 269-2, Moffett Field, California 94035, USA
  • 3Department of Physics, Lancaster University, Lancaster LA1 4YB, UK
  • 4Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia

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Vol. 72, Iss. 2 — August 2005

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