A comparison of linear respiratory system models based on parameter estimates from PRN forced oscillation data

The forced oscillation technique offers some advantages over spirometry for assessing pulmonary function. It requires only passive patient cooperation; it also provides data in a form, frequency-dependent impedance, which is very amenable to engineering analysis. In particular, the data can be used to obtain parameter estimates for electric circuit-based models of the respiratory system, which can in turn aid the detection and diagnosis of various diseases/pathologies. In this study, we compare the least-squares error performance of the RIC, extended RIC, augmented RIC, augmented RIC+I(p), DuBois, Nagels and Mead models in fitting 3 sets of impedance data. These data were obtained by pseudorandom noise forced oscillation of healthy subjects, mild asthmatics and more severe asthmatics. We found that the aRIC+I(p) and DuBois models yielded the lowest fitting errors (for the healthy subjects group and the 2 asthmatic patient groups, respectively) without also producing unphysiologically large component estimates.