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

[thumbnail of PRE2005ModelDiscovery.pdf]
PDF (PRE2005ModelDiscovery.pdf)
PRE2005ModelDiscovery.pdf - Published Version

Download (597kB)


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:
Journal Article
Journal or Publication Title:
Physical Review E
Uncontrolled Keywords:
?? cardiovascular systempneumodynamicsstatistical analysisstochastic processesnonlinear dynamical systemsstatistical and nonlinear physicsstatistics and probabilitycondensed matter physicsqc physics ??
ID Code:
Deposited By:
Deposited On:
06 Jun 2008 15:48
Last Modified:
18 Dec 2023 01:12