title: Evaluation of respiratory system models based on parameter estimates from impulse oscillometry data.
abstract: Impulse oscillometry offers advantages over spirometry because it requires minimal patient cooperation, it yields pulmonary function data in a form that is readily 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 in turn may assist the detection and diagnosis of various diseases/pathologies. Of the six models analyzed during this study, Mead's model seems to provide the most robust and accurate parameter estimates for our data set of 5 subjects with airflow obstruction including asthma and chronic obstructive pulmonary disease and another 5 normal subjects with no identifiable respiratory disease. Such a diagnostic approach, relying on estimated parameter values from a respiratory system model estimate and the degree of their deviation from the normal range, may require additional measures to ensure proper identification of diseases/pathologies but the preliminary results are promising.
authors.affiliation: Department of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.
authors.forename: S
authors.initials: S
authors.lastname: Baswa
authors.forename: H
authors.initials: H
authors.lastname: Nazeran
authors.forename: P
authors.initials: P
authors.lastname: Nava
authors.forename: B
authors.initials: B
authors.lastname: Diong
authors.forename: M
authors.initials: M
authors.lastname: Goldman