title: A comparison of linear respiratory system models based on parameter estimates from PRN forced oscillation data.
abstract: 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.
authors: Department of Engineering, Texas Christian University, Fort Worth, TX 76129, USA.
authors: B
authors: B
authors: Diong
authors: J
authors: J
authors: Grainger
authors: M
authors: M
authors: Goldman
authors: H
authors: H
authors: Nazeran
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000175:diagnosis* / Q000473:pathology; D001698:Biomedical Engineering / Q000379:methods; D016022:Case-Control Studies; D004867:Equipment Design; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D008962:Models, Theoretical; D009991:Oscillometry / Q000379:methods*; D015203:Reproducibility of Results; D012119:Respiration; D012815:Signal Processing, Computer-Assisted