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A simple cycle-averaged model for cardiovascular dynamics.

Tushar Parlikar1, George Verghese

  • 1Laboratory for Electromagn. & Electron. Syst., MIT, Cambridge, MA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
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This study introduces a cycle-averaging method to simplify cardiovascular models. The new model effectively tracks long-term hemodynamic trends with minimal error, improving patient monitoring.

Area of Science:

  • Biomedical Engineering
  • Computational Physiology
  • Cardiovascular Dynamics

Background:

  • Lumped-parameter, time-varying electrical circuit analogs are common for simulating cardiovascular hemodynamics.
  • Pulsatile models offer detailed intracycle (beat-by-beat) dynamics.
  • Tracking long-term hemodynamic trends requires focusing on intercycle dynamics.

Purpose of the Study:

  • To develop a cycle-averaged cardiovascular model for analyzing intercycle dynamics.
  • To adapt existing pulsatile models for long-term hemodynamic trend analysis.
  • To assess the approximation errors of the cycle-averaged model.

Main Methods:

  • Applied a cycle-averaging method to a simple pulsatile cardiovascular model.
  • Derived a new model focusing on intercycle dynamics.

Related Experiment Videos

  • Evaluated model performance under parameter perturbations, including systemic vascular resistance.

    • Main Results:

    • Successfully derived a cycle-averaged model for cardiovascular dynamics.
    • The cycle-averaged model accurately captures intercycle dynamics.
    • Relatively small approximation errors were observed across various parameter perturbations.

    Conclusions:

    • Cycle-averaging is a viable method for simplifying cardiovascular models.
    • The derived model is suitable for tracking long-term hemodynamic trends in clinical settings.
    • This approach enhances the utility of computational models for patient monitoring over time.