Two volunteers undergo whole-body plethysmography and spirometry assessments. Respectively, the methods measure total and specific airway resistance along with several lung volumes and capacities. Spirometer and plethysmograph data may noninvasively index and formulate graphical representations of lung functions for the interpretation of lung health. Nevertheless, spirometry and plethysmography may just be the start of many pulmonary function tests (PFTs) necessary to diagnose a particular disease. For instance, investigating lung compliance reveals information not available through spirometry and plethysmography that may lead to the diagnosis of fibrosis. The downside, having to take multiple PFT is time consuming, costly, and the subject may find some test to be unpleasant. Fitting models to respiratory data may provide an alternative to some pulmonary function test. Modeling reveals hidden information already present from current test. The experiment performs regression analysis using several first principle models on respiratory data from plethysmography. The models are fit to data and the goodness-of-fit for the models distinguish between airway obstruction and normal lung mechanics.
|Commitee:||Bill, Andreas, Druzgalski, Christopher|
|School:||California State University, Long Beach|
|Department:||Physics and Astronomy|
|School Location:||United States -- California|
|Source:||MAI 56/03M(E), Masters Abstracts International|
|Subjects:||Applied Mathematics, Biomedical engineering, Pathology|
|Keywords:||Model, Pathology, Plethysmography, Regression, Residual, Respiratory|
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