Optimal selection of frequencies for estimating parameters from respiratory impedance data

The goal of this study was to evaluate whether optimal selection of a reduced number of frequency points would still result in statistically reliable parameter estimates. A direct-search technique is described which optimally places a small number of frequencies so that the volume of the parameter joint confidence region is minimized. The accuracy of the parameters estimated from a full data set (50 evenly spaced points) is compared to that achievable with optimal designs using 20, 10, or 5 frequency points. The techniques were applied to parameters obtained from healthy dogs and humans. Results indicated that with ten optimally chosen frequencies most parameter uncertainties are only slightly higher than that achievable with 50 frequencies while parameter uncertainties increase greatly when only five optimal points are used. This suggests that the technique of forced oscillation permits identification of the distribution of respiratory system properties without the need for extensive data acquisition.