Measurement of the mechanical impedance of the respiratory system provides information about its mechanical properties which relate to the physical state of its various structural elements. A goal of several studies in the past has been to extract estimates of a number of physical descriptors from pulmonary, or respiratory impedance data obtained over a range of frequencies. The numerical and statistical techniques most often used to extract these parameter estimates do so from an assumed mechanical model of the respiratory system. Several models have been proposed and used to analyze impedance data. However, in none of these studies, have well defined anatomical correlates to these parameters been established by parallel physiological measurements. The overall goal of the proposed research is to test the hypothesis that the physical parameters extracted from impedance data have specific and identifiable anatomical correlates that can be established experimentally. To accomplish this goal we propose to measure respiratory and pulmonary impedances in normal dogs from 4 to 96 Hz and from these data extract estimates of the respiratory and lung parameters using nonlinear regression analysis techniques. Specific interventions will be used to alter the mechanical properties of the respiratory system in a predictable fashion and the measurements repeated. Parameter estimates resulting from analysis of the impedance data in the control and altered conditions will be compared to those measured in the animals by other independent established methods. From these comparisons we will be able to determine whether the parameters extracted from impedance data have specific anatomic correlates. The models used to extract these parameter estimates will be thoroughly investigated by numerical studies using theoretically generated data. These numerical studies will indicate over what frequency range unique values for the parameters in a given model can be estimated from impedance data. Sensitivity analyses will be employed to determine how, and at which frequencies, each of the various parameters influences the impedance data, and to determine the accuracy with which each parameter can be established from a given set of impedance data.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Respiratory and Applied Physiology Study Section (RAP)
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Boston University
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