DocumentCode :
2741550
Title :
Classification of Pulmonary Diseases Based on Impulse Oscillometric Measurements of Lung Function Using Neural Networks
Author :
Barúa, Miroslava ; Nazeran, Homer ; Nava, Patricia ; Granda, Virginia ; Diong, Bill
Author_Institution :
Department of Electrical and Computer Engineering, University of Texas at El Paso El Paso, TX USA, Email address: miroslav@utep.edu
Volume :
2
fYear :
2004
fDate :
1-5 Sept. 2004
Firstpage :
3848
Lastpage :
3851
Abstract :
Central and peripheral airflow obstructions frequently occur in patients with chronic obstructive lung disease or asthma and may have different pathophysiological mechanisms of obstruction and require different therapeutic inte rventions. Impulse oscillometry (IOS) is a patient-friendly method for studying respiratory function in health and disease. The enormous variety of patterns and the high degree of variability in the measured lung function parameters has made the automated diagnosis of pulmonary diseases very desirable by pulmonary physiologists and clinicians. Computer aided diagnosis can serve as a second but quantitative opinion to diagnosis and screening.
Keywords :
Impulse Oscillometry (IOS); artificial neural networks; backpropagation algorithm; pulmonary disease classification; Artificial neural networks; Backpropagation algorithms; Diseases; Electric variables measurement; Electrical resistance measurement; Fuzzy logic; Loudspeakers; Lungs; Neural networks; Software tools;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Print_ISBN :
0-7803-8439-3
Type :
conf
DOI :
10.1109/IEMBS.2004.1404077
Filename :
1404077
Link To Document :
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