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Parameter identification in coronary pressure flow models: a graphical approach

P A Harris1, S Bosan, T R Harris

  • 1Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235.

Annals of Biomedical Engineering
|November 1, 1994
PubMed
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A new graphical method aids in identifying parameters in physiological models by visualizing model behavior and parameter interactions. This approach successfully distinguished parameters in a coronary circulation model, improving model application.

Area of Science:

  • Physiological modeling
  • Computational biology
  • Biomedical engineering

Background:

  • Parameter identification is crucial for physiological models but complicated by parameter interactions.
  • Model sensitivity to parameters often depends on other model parameters.

Purpose of the Study:

  • To present a graphical approach for visualizing parameter identification in physiological models.
  • To demonstrate the technique's utility in distinguishing interdependent parameters.

Main Methods:

  • Developed a graphical method to visualize model behavior, sensitivity functions, and parameter interdependence.
  • Applied the technique to a four-parameter myocardial pump model of the coronary circulation.
  • Regressed upon coronary inflow and outflow to uniquely identify all parameters.

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Main Results:

  • The graphical approach allows visualization across parameter space.
  • Successfully distinguished four key parameters of the coronary circulation model: alpha (resistance ratio), R (vascular bed resistance), C (vascular bed compliance), and kappa (pressure relationship).
  • Unique identification of all parameters required analysis of both coronary inflow and outflow.

Conclusions:

  • The graphical method offers a practical tool for parameter identification in complex physiological models.
  • This technique enhances the understanding of parameter interdependence and model behavior.
  • The study highlights the necessity of utilizing both inflow and outflow data for comprehensive parameter identification in this model.