• 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