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Proceedings Paper

Cardiovascular oscillations: in search of a nonlinear parametric model
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Paper Abstract

We suggest a fresh approach to the modeling of the human cardiovascular system. Taking advantage of a new Bayesian inference technique, able to deal with stochastic nonlinear systems, we show that one can estimate parameters for models of the cardiovascular system directly from measured time series. We present preliminary results of inference of parameters of a model of coupled oscillators from measured cardiovascular data addressing cardiorespiratory interaction. We argue that the inference technique offers a very promising tool for the modeling, able to contribute significantly towards the solution of a long standing challenge -- development of new diagnostic techniques based on noninvasive measurements.

Paper Details

Date Published: 30 April 2003
PDF: 11 pages
Proc. SPIE 5110, Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems, (30 April 2003); doi: 10.1117/12.497056
Show Author Affiliations
Andriy Bandrivskyy, Lancaster Univ. (United Kingdom)
Dmitry Luchinsky, Lancaster Univ. (United Kingdom)
Peter V.E. McClintock, Lancaster Univ. (United Kingdom)
Vadim Smelyanskiy, NASA Ames Research Ctr. (United States)
Aneta Stefanovska, Univ. of Ljubljana (Slovenia)
Lancaster Univ. (United Kingdom)
Dogan Timucin, NASA Ames Research Ctr. (United States)


Published in SPIE Proceedings Vol. 5110:
Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems
Sergey M. Bezrukov; Hans Frauenfelder; Frank Moss, Editor(s)

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