Time-Varying Cardiovascular Oscillations

  • V. N. Smelyanskiy
  • D. A. Timucin
  • D. G. Luchinsky
  • A. Stefanovska
  • A. Bandrivskyy
  • P. V. E. McClintock
Conference paper
Part of the Solid Mechanics and Its Applications book series (SMIA, volume 110)


Signals derived from the human cardiovascular system (CVS) are exceptionally complex, being time-varying, noisy, and of necessarily limited duration. Yet an appropriate analysis of them may be expected to yield detailed information about the dynamics of the underlying physiological processes. A new approach to the analysis and modelhng of CVS signals is proposed. It combines decomposition of the signals into principal modes and a novel method of parameter identification in nonlinear stochastic systems based on Bayesian inference. The scheme is tested on a noisy Van der Pol oscillator, for which it yields rapid convergence and correct inference of the known parameters. Preliminary applications to CVS data are discussed.


Probability Density Function Empirical Mode Decomposition Oscillatory Component Observe Time Series Nonlinear Stochastic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • V. N. Smelyanskiy
    • 1
  • D. A. Timucin
    • 1
  • D. G. Luchinsky
    • 2
  • A. Stefanovska
    • 2
    • 3
  • A. Bandrivskyy
    • 2
  • P. V. E. McClintock
    • 2
  1. 1.NASA Ames Research Center, MS 269-2Moffett FieldUSA
  2. 2.Department of PhysicsLancaster UniversityLancasterUK
  3. 3.Group of Nonlinear Dynamics and Synergetics, Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia

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