A Comparison of Various Respiratory System Models Based on Parameter Estimates From Impulse Oscillometry Data

Impulse oscillometry offers an advantage over spirometry when conducting pulmonary function tests. Not only does it require minimal patient cooperation, it provides useful data in a form amenable to engineering methods. 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. Of the six models analyzed during this study, the DuBois model and a newly proposed extended RIC model seem to provide the most robust parameter estimates for our entire data set of 106 subjects with various respiratory ailments such as asthma and chronic obstructive pulmonary disease. Such a diagnostic approach, relying on estimated parameter values, may require additional measures to ensure proper identification of diseases/pathologies but the preliminary results are promising.