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

作者信息

Baswa S, Nazeran H, Nava P, Diong B, Goldman M

机构信息

Department of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.

出版信息

Conf Proc IEEE Eng Med Biol Soc. 2005;2005:2958-61. doi: 10.1109/IEMBS.2005.1617094.

DOI:10.1109/IEMBS.2005.1617094
PMID:17282863
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.

摘要

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