Influence of experimental conditions on parameter estimation for breathing mechanics: A sensitivity analysis approach

  • G. Avanzolini
  • P. Barbini
Physiological Measurement


Sensitivity analysis techniques are applied to two classic linear models of breathing mechanics to evaluate the expected accuracy of the parameter estimates to be obtained in different experimental conditions. For this reason the characteristics of the so-called indifference region have been studied, in which the objective function is not significantly affected by changes, in the parameter values. A Mead type or a Otis type configuration was used to characterise the breathing mechanics by computer simulation. For both models four different specific experiments have been simulated which correspond to two types of ventilation (spontaneous breathing and mechanical ventilation) and to two different pathologies (obstructive lung disease and pulmonary rigidness). The results obtained show that the use of the signals which are typical of mechanical ventilation allows very accurate parameter estimates to be obtained in both cases. On the other hand, in spontaneous breathing the estimation of the parameters is more critical, and in this case the Mead model is practically unusable whereas the Otis model supplies results which are still acceptable, even if very sensitive to changes in pathology.


Breathing mechanics Parameter estimation Sensitivity analysis 


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  1. Avanzolini, G. andBarbini, P. (1982) Comments on ‘Estimating respiratory mechanical parameters in parallel compartment models’.IEEE Trans.,BME-29, 772–774.Google Scholar
  2. Avanzolini, G. andBarbini, P. (1984a) A versatile identification method applied to analysis of respiratory mechanics.,BME-31, 520–526.Google Scholar
  3. Avanzolini, G. andBarbini, P. (1984b) Sensitivity analysis for an improved estimation of respiratory mechanics parameters.J. Biomed. Eng.,6, 189–194.Google Scholar
  4. Avanzolini, G. andBarbini, P. (1985) A comparative evaluation of three on-line identification methods for a respiratory mechanical model.IEEE Trans.,BME-32, 957–963.Google Scholar
  5. Bekey, G. A. andBeneken, J. E. W. (1978) Identification of biological systems: a survey.Automatica,14, 41–47.MATHCrossRefGoogle Scholar
  6. Carson, E. R., Cobelli, C. andFinkelstein, L. (1981) Modeling and identification of metabolic systemsAm. J. Physiol.,240, 120–129.Google Scholar
  7. Draper, N. R. andSmith, H. (1981)Applied regression analysis, 2nd edn., J. Wiley & Sons, New York.MATHGoogle Scholar
  8. Eyles, J. G. andPimmel, R. L. (1981) Estimating respiratory mechanical parameters in parallel compartment models.IEEE Trans.,BME-28, 313–317.Google Scholar
  9. Eyles, J. G. andPimmel, R. L. (1982) Authors’ reply to ‘Comments on estimating respiratory parameters in parallel compartment models’.,BME-29, 774.Google Scholar
  10. Eyles, J. G., Pimmel, R. L., Fullton, J. M. andBromberg, P. A. (1982) Parameter estimates in a five-element respiratory mechanical model.,BME-29, 460–463.Google Scholar
  11. Eyles, J. G. andPimmel, R. L. (1983) A rapidly converging algorithm for estimating respiratory mechanical parameters in a five-element model.,BME-30, 675–679.Google Scholar
  12. Lutchen, K. R. andSaidel, G. M. (1982) Sensitivity analysis and experimental design techniques: application to nonlinear, dynamic lung models.Comput Biomed. Res.,15, 434–454.CrossRefGoogle Scholar
  13. Lutchen, K. R., Primiano, F. P. andSaidel, G. M. (1982) A nonlinear model combining pulmonary mechanics and gas concentration dynamics.IEEE Trans.,BME-29, 629–641.Google Scholar
  14. Mead, J. (1969) Contribution of compliance of airways to frequency-dependent behavior of lungs.J. Appl. Physiol.,26, 670–673.Google Scholar
  15. Michaelson, E. D., Grasman, E. D. andPeters, W. R. (1975) Pulmonary mechanics by spectral analysis of forced random noise.J. Clin. Invest.,56, 1210–1230.CrossRefGoogle Scholar
  16. Otis, A. B., McKerrow, C. B., Bartlett, R. A., Mead, J., McIlroy, M. B., Selverstone, N. J. andRadford, E. P. (1956) Mechanical factors in distribution of pulmonary ventilation.J. Appl. Physiol.,8 427–443.Google Scholar
  17. Paulsen, R. A. Clark, J. W. Jr.,Murphy, P. H. andBurdine, J. A. (1982) Sensitivity analysis and improved identification of a systemic arterial model.IEEE Trans.,BME-29, 164–178.Google Scholar
  18. Tsai, M. J. andPimmel, R. L. (1979) Computation of respiratory resistance, compliance, and inertance from forced oscillatory impedance data.,BME-26, 492–493.Google Scholar

Copyright information

© IFMBE 1987

Authors and Affiliations

  • G. Avanzolini
    • 1
  • P. Barbini
    • 2
  1. 1.Dipartimento di Eletronica, Informatica e SistemisticaUniversity of BolognaBolognaItaly
  2. 2.Sezione di Bioingegneria, Istituto di, Chirurgia Toracica e CardiovascolareUniversity of SienaSienaItaly

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