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Parameter estimation and sensitivity analysis of a nonlinearly elastic static lung model.

J R Ligas, G M Saidel, F P Primiano

    Journal of Biomechanical Engineering
    |November 1, 1985
    PubMed
    Summary

    This study developed a lung mechanics model to analyze static pressure-volume behavior. The model accurately fits data and establishes reliable parameter ranges, applicable to other physiological systems.

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    Area of Science:

    • Pulmonary Mechanics
    • Biophysics
    • Computational Biology

    Background:

    • Understanding lung parenchyma mechanics is crucial for respiratory physiology.
    • Existing models may lack robust parameter estimation and sensitivity analysis.
    • Static pressure-volume (P-V) behavior provides insights into lung tissue elasticity.

    Purpose of the Study:

    • To test a pseudo-elastic strain energy function model for static lung P-V behavior.
    • To estimate model parameters and their variances using optimal least-squares fitting.
    • To develop criteria for selecting reliable parameter estimates and define normal parameter ranges.

    Main Methods:

    • Utilized a pseudo-elastic strain energy function to model lung parenchyma.
    • Employed optimal least-squares fitting to match model predictions with experimental P-V data from excised dog lungs.

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  • Performed sensitivity analysis using the Hessian matrix to assess parameter reliability.

    • Main Results:

    • The model demonstrated a good fit to static P-V data from twelve dog lungs.
    • Significant variations in coefficients of variation for parameter values were observed.
    • Established formal criteria for selecting reliable parameter estimates and their variances based on Hessian analysis.
    • Defined a normal range of parameter values for the studied dog lungs.

    Conclusions:

    • The model successfully describes static lung P-V data and estimates fundamental constitutive parameters.
    • Sensitivity analysis provides a robust method for parameter selection and variance assessment.
    • The developed parameter estimation and sensitivity analysis techniques have broad applicability to other physiological systems.