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.

A study of IOS data using the aRIC+I(p) model of respiratory impedance.

Development of better methods to assess human lung function has been continuing since the existing standard lung function test of spirometry requires subjects to inhale and exhale with maximum effort, which may be troublesome especially for the elderly and young children, leading to unreliable results. Therefore, the method of forced oscillation, and the Impulse Oscillometry System (IOS) in particular, has been developed to lessen the effort of the patients while obtaining valid measurements. The applied pressure waves and the resulting airflow responses are recorded to provide information about the respiratory system's input impedance, which can be fit by electric circuit models to possibly serve as a means to detect and diagnose respiratory diseases. Presently, research continues to find a more accurate model that also provides reasonable component values. This paper proposes the augmented RIC+I(p)(aRIC+I(p)) model and compares it to five other well-known models (the RIC, extended RIC, augmented RIC, DuBois and Mead models) in fitting the IOS data from adult COPD patients and healthy subjects. While the aRIC+I(p) model yielded slightly higher fitting error than the Mead and DuBois models, it did not produce unphysiologically large values for any of its components, unlike the Mead and DuBois models. Hence, the aRIC+(p) model appears to be the most reasonable one for use, at this point in time, in studying IOS-based computer-aided detection and diagnosis of COPD.

A study of IOS data using two mead-related models of respiratory impedance.

This paper introduces two new respiratory system models, the Mead-Cw model and the Mead-Cl model, which are 6-component models that are intermediate in complexity between the well-known 7-component Mead model and the recently proposed 5-component augmented RIC model (derived from the Mead model by eliminating both Cw and Cl). Their modeling errors were compared to the RIC, extended RIC, augmented RIC and Mead models, for component values estimated from IOS data. The two new models yielded lower errors than all the other models, except for the Mead model. However, the Mead-Cl model and the Mead-Cw model also yielded unreasonably large values for Cw and Cl, respectively, which are known disadvantages of the Mead model. Hence the augmented RIC model appears to be the most useful at present for IOS-based computer-aided detection and diagnosis of respiratory disorders.

Modeling human respiratory impedance in Hispanic asthmatic children.

Central (large airway) and peripheral (small airway) dysfunction frequently occur in patients with asthma and chronic obstructive lung disease. Measurement of the respiratory impedance can assist with diagnosis of pathological conditions. The forced Oscillation technique (FOT) superimposes small pressure perturbations at the mouth during tidal breathing of a subject to measure lung mechanical parameters. The Impulse Oscillometry System (IOS) is a commercial instrument that measures forced oscillatory impedance. IOS can be conveniently used in children as it only requires their passive cooperation during pulmonary function testing. Forced oscillatory impedance can be analyzed with respiratory system equivalent electrical circuit models. Models of varying complexity and fidelity have been developed to provide better understanding of respiratory mechanics and enable greater specificity of the diagnosis. Parameter estimates for these models can be used as reference values for detection and diagnosis of different respiratory pathologies. Previous work by our group has evaluated several known respiratory models and a new RIC model (augmented RIC) has emerged which offers advantages over earlier models. It has been shown that one parameter of this new model (representing peripheral airway compliance) is capable of discriminating between normal and asthmatic children. In this paper, we analyzed IOS data from 40 Hispanic asthmatic children and obtained sensitive impulse oscillometric parameters of lung function as well as parameter estimates for the augmented RIC (aRIC) model to distinguish between constricted (asthmatic condition) and non-constricted (non-asthmatic condition) airways with very promising results.

Can asthma in children be detected by the estimated parameter values of the augmented RIC model?

This paper describes the estimation of the parameter values for the recently introduced augmented RIC respiratory system model from impulse oscillometry data obtained from both asthmatic and normal children. An analysis of these values has indicated that one of the capacitance parameters of the model provides good discrimination between these two groups of children; moreover, this finding corresponds well with current medical understanding of the pathology of asthma.

Evaluation of respiratory system models based on parameter estimates from impulse oscillometry data.

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.

Parameter estimation by descent and genetic algorithm methods of an in-vitro stenosis bypass model.

The development of improved hemodynamic impedance models can greatly aid the understanding of arterial disease progression and its remediation. This paper leverages the recent progress in advanced manufacturing techniques to engage in in-vitro experimentation with physiologically relevant geometries and flows that correspond to arterial stenosis. The measurements of pressures and flow obtained by these experiments were then used to estimate flow-to-pressure transfer functions, aimed at determining a lumped-parameter impedance model of the physical system, by applying conventional descent as well as recently developed Genetic Algorithm methods. The resulting transfer functions can now be utilized for further studies with regard to the hemodynamics relevant to arterial stenosis.

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.