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Sensitivity analysis of the systemic circulation with a view to computer simulation and parameter estimation.

G Avanzolini1, P Barbini, A Cappello

  • 1Dipartimento di Elettronica, University of Bologna, Italy.

Journal of Biomedical Engineering
|January 1, 1989
PubMed
Summary
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This study simplifies a seven-parameter vascular model for better simulation and estimation. A reduced five-parameter model focusing on resistance and compliance is recommended for efficient circulatory system analysis.

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Systems Biology

Background:

  • The systemic vascular bed is complex, requiring simplified models for analysis.
  • Accurate modeling is crucial for understanding cardiovascular dynamics and disease.
  • Existing models may be too complex for efficient simulation and parameter estimation.

Purpose of the Study:

  • To perform a sensitivity analysis on a seven-parameter systemic vascular bed model.
  • To identify appropriate structure reductions for simulation and estimation applications.
  • To guide the selection of simplified models for clinical and research use.

Main Methods:

  • Sensitivity analysis was conducted on a seven-parameter model.
  • Systolic and diastolic transfer functions were analyzed separately.

Related Experiment Videos

  • Non-linear problems were linearized for distinct analysis.
  • Nominal parameter values for normal human circulation were used.

    • Main Results:

    • Inertial effects were found to have minimal influence on the model.
    • Simple resistance-compliance models are suitable for systemic vascular bed analysis.
    • A five-parameter model is advantageous for simulation purposes.
    • Further reduction to a three-element model (peripheral resistance, arterial compliance, venous compliance) is suggested for parameter estimation.

    Conclusions:

    • Reduced-order models of the systemic vascular bed are feasible and beneficial.
    • Simplified resistance-compliance models effectively capture essential circulatory dynamics.
    • The choice of model complexity should be application-dependent (simulation vs. estimation).