Abstract
Time-domain estimation has been invoked for tracking of respiratory mechanical properties using primarily a simple single-compartment model containing a series resistance (R rs) and elastance (E rs). However, owing to the viscoelastic properties of respiratory tissues,R rs andE rs exhibit frequency dependence below 2 Hz. The goal of this study was to investigate the bias and statistical accuracy of various time-domain approaches with respect to model properties, as well as the estimated impedance spectra. Particular emphasis was placed on establishing the tracking capability using a standard step ventilation. A simulation study compared continuous-timeversus discrete-time approaches for both the single-compartment and two-compartment models. Data were acquired in four healthy humans and two dogs before and after induced severe pulmonary edema while applying sinusoidal and standard ventilator forcing.R rs andE rs were estimated either by the standard Fast Fourier Transform (FFT) approach or by a time-domain least square estimation. Results show that the continuous-time model form produced the least bias and smallest parameter uncertainty for a single-compartment analysis and is quite amenable for reliable on-line tracking. The discrete-time approach exhibits large uncertainty and bias, particularly with increasing noise in the flow data. In humans, the time-domain approach produced smooth estimates ofR rs andE rs spectra, but they were statistically unreliable at the lower frequencies. In dogs, both the FFT and time-domain analysis produced reliable and stable estimates forR rs orE rs spectra for frequencies out to 2 Hz in all conditions. Nevertheless, obtaining stable on-line parameter estimates for the two-compartment viscoelastic models remained difficult. We conclude that time-domain analysis of respiratory mechanics should invoke a continuous-time model form.
Article PDF
Similar content being viewed by others
References
Avanzolini, G., and P. Barbini. A versatile identification method applied to analysis of respiratory mechanics.IEEE Trans. Biomed. Eng. 31:520–526, 1984.
Avanzolini, G., and P. Barbini. A comparative evaluation of three on-line identification methods for a respiratory mechanical model.IEEE Trans. Biomed. Eng. 32:957–963, 1985.
Avanzolini, G., P. Barbini, A. Cappello, and G. Cevenini. Real-time tracking of parameters of lung mechanics: Emphasis on algorithm tuning.J. Biomed. Eng. 12:489–495, 1990.
Barbini, P., A. Cappello, and G. Cevenini. Real-time tracking of breathing parameters in mechanically ventilated dogs. In: Proc. 10th Annu. IEEE-EMBS Conf., 1988, pp. 700–703.
Bard, Y. Nonlinear Parameter Estimation. New York: Academic Press, 1974, 341 pp.
Barnas, G. M., D. Stamenovic, K. R. Lutchen, and C. F. Mackenzie. Lung and chest wall impedances in the dog: Effects of frequency and tidal volume.J. Appl. Physiol. 72:87–93, 1991.
Barnas, G. M., G. Ho, M. Green, P. Harinath, A. Smally, D. N. Cambell, and J. E. Mendham. Effects of analysis method and forcing waveform on measurement of respiratory mechanics.Resp. Physiol. 89:263–285, 1992a.
Barnas, G. M., P. J. Mills, C. F. Mackenzie, S. J. Fletcher, and M. Green. Effect of tidal volume on respiratory system elastance during anesthesia and paralysis.Am. Rev. Resp. Dis. 145:522–526, 1992b.
Barnas, G. M., D. Stamenovic, and K. R. Lutchen. Lung and chest wall impedance in the dog in the normal range of breathing: Effects of pulmonary edema.J. Appl. Physiol. 73:1040–1046, 1992c.
Bates, J. H. T., M. Decramer, W. A. Zin, A. Harf, J. Millic-Emili, and H. K. Chang. Respiratory resistance with histimine challenge by single-breath and forced oscillation methods.J. Appl. Physiol. 61:873–880, 1986.
Bates, J. H. T., and A.-M. Lauzon. A nonstatistical approach to estimating confidence intervals about model parameters: Application to respiratory mechanics.IEEE Trans. Biomed. Eng. 39:94–100, 1992.
Chapman, F. W., and J. C. Newell. Estimating lung mechanics of the dog with unilateral lung injury.IEEE Trans. Biomed. Eng. 36:405–413, 1989.
Haykin, S. S. A unified treatment of recursive digital filtering.IEEE Trans. Autom. Cntrl. 17:113–116, 1972.
Lauzon, A.-M., and J. H. T. Bates. Estimation of timevarying respiratory mechanical parameters by recusive least squares.J. Appl. Physiol. 71:1159–1165, 1991.
Lutchen, K. R. Sensitivity analysis of respiratory parameter uncertainties: Impact of criterion function form and constraints.J. Appl. Physiol. 69:766–775, 1990.
Lutchen, K. R., and A. C. Jackson. Statistical measures of parameter estimates from models fit to respiratory impedance data: Emphasis on joint variabilities.IEEE Trans. Biomed. Eng. 33:1000–1010, 1986.
Lutchen, K. R., and A. C. Jackson. Effects of tidal volume and methacholine on low frequency total respiratory impedance in dogs.J. Appl. Physiol. 68:2128–2138, 1990.
Lutchen, K. R., Z. Hantos, and A. C. Jackson. Importance of low-frequency impedance data for reliably quantifying parallel inhomogeneities of respiratory mechanics.IEEE Trans. Biomed. Eng. 35:472–481, 1988.
Lutchen, K. R., D. W. Kaczka, B. Suki, G. M. Barnas, G. Cevenini, and P. Barbini. Low frequency respiratory mechanics using ventilator-driven forced oscillations.J. Appl. Physiol. 75:2549–2560, 1993.
Michaelson, E. D., E. D. Grassman, and W. R. Peters. Pulmonary mechanics by spectral analysis of forced random noise.J. Clin. Invest. 56:1210–1230, 1975.
Montgomery, D. C., and E. A. Peck. Introduction to Linear Regression Analysis. New York: John Wiley & Sons, Inc., 1992, 527 pp.
Peslin, R. J., F. da Silva, F. Chabot, and C. Duvivier. Respiratory mechanics studied by multiple linear regression in unsedated ventilated patients.Eur. Resp. J. 5:871–878, 1992.
Polese, G., A. Rossi, L. Appendini, G. Brandi, J. H. T. Bates, and R. Brandolese. Partitioning of respiratory mechanics in mechanically ventilated patients.J. Appl. Physiol. 71:2425–2433, 1991.
Sato, J., B. L. K. Davey, F. Shardonofsky, and J. H. T. Bates. Low-frequency respiratory resistance in the normal dog during mechanical ventilation.J. Appl. Physiol. 70:1536–1543, 1991.
Sinha, N. K. Estimation of transfer function of continuous system from sampled data.Proc. IEEE 119:612–614, 1972.
Uhl, R. R., and F. J. Lewis. Digital computer calculation of human pulmonary mechanics using a least squares fit technique.Comp. Biomed. Res. 7:489–495, 1974.
Wald, A., D. Jason, T. W. Murphy, and V. D. B. Mazzia. A computer system for respiratory parameters.Comp. Biomed. Res. 2:411–429, 1969.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Kaczka, D.W., Barnas, G.M., Suki, B. et al. Assessment of time-domain analyses for estimation of low-frequency respiratory mechanical properties and impedance spectra. Ann Biomed Eng 23, 135–151 (1995). https://doi.org/10.1007/BF02368321
Received:
Revised:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02368321