Evaluation of Respiratory System Models Based on Parameter Estimates from Impulse Oscillometry Data S. Baswa 1 , B. Diong 2 , H. Nazeran 1 , P. Nava 1 and M. Goldman 3 1 Department of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA 2 Texas Christian University, Fort Worth, TX 76129, USA 3 Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90034, USA 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. Keywords—Respiratory impedance, respiratory resistance, respiratory reactance I. INTRODUCTION Lung function is most commonly assessed by standard spirometric pulmonary function tests. However, spirometric measurements require maximal coordinated inspiratory and expiratory efforts. The considerable degree of cooperation required from the patient makes spirometry inappropriate for young children and older adults. In contrast, respiratory function assessment by the method of forced oscillation [1] requires minimal patient cooperation, and only ‘passive cooperation,’ while wearing a nose clip to close the nares, and breathing normally. Air pressure and rate of air flow at the entrance to the respiratory system are measured, thereby defining its mechanical impedance. In particular, the Impulse Oscillometry System (IOS) is a commercially available product for measuring forced oscillatory impedance by employing brief 60-70 millisecond pulses of pressure. IOS measurements yield frequency-dependent impedance values which may be correlated with models consisting of electrical components that are analogous to the resistances, compliances and inertances inherent in the respiratory system. Consequently, parameter estimates for such respiratory system models [2] can then serve as quantitative measures for better detection and diagnosis of various diseases/pathologies. This paper describes work to identify the most appropriate model(s) to use for respiratory system disease detection and diagnosis. Using IOS data for adults with respiratory disease, we have examined the performance of six respiratory models by estimating their parameters and calculating the corresponding estimation error. Although this work is similar in some respects to one described earlier [3], there are the following significant differences: data for the present work include responses of normal adults in addition to adults with respiratory ailments, data for the present work was collected recently in the U.S. and Australia, while data for the previous work was collected a few years ago in Australia, so differences exist in equipment, technical personnel, measurement technique, data for the present work resulted from several visits and multiple tests of the same patient (with a series of tests performed during each visit) while data for the previous work consisted of only one test result per patient, data for the present work includes responses to inhalation of dry powdered mannitol in addition to baseline measurements, while data for the previous work did not include such responses, and a subtle but important change has been made to the implementation of the analytical technique, which is detailed in Section III. II. DESCRIPTION OF THE MODELS Of the six models used to fit the data for each patient, considerable work has previously been done on the DuBois model [1], [4], [5], and [6] while Schmidt et al. [2] analyzed three of these models (RC, RIC and Mead’s) with respect to infant data. Another of these models (extended RIC) has been introduced only recently [3]. A. RC model The resistance of the airways and the compliance of the alveoli are modeled as a simple RC circuit (Fig. 1 with R in cmH 2 O/L/s, C in L/cmH 2 O) with impedance [2] given by C j R Z (1) C R Fig. 1. RC model. where is the angular frequency in radians/second. This model can be used with tidal breathing measurements, but is insufficient for higher frequency analysis [2]. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005 0-7803-8740-6/05/$20.00 ©2005 IEEE.