High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements.

Journal: Computer methods and programs in biomedicine
Published Date: Mar 28, 2017

Abstract

BACKGROUND AND OBJECTIVES: The main pathologic feature of asthma is episodic airway obstruction. This is usually detected by spirometry and body plethysmography. These tests, however, require a high degree of collaboration and maximal effort on the part of the patient. There is agreement in the literature that there is a demand of research into new technologies to improve non-invasive testing of lung function. The purpose of this study was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the forced oscillation technique (FOT) in the diagnosis of airway obstruction in patients with asthma.

Authors

  • Jorge L M Amaral
    Department of Electronics and Telecommunications Engineering, Brazil.
  • Agnaldo J Lopes
    Pulmonary Function Laboratory, Pedro Ernesto University Hospital, Brazil.
  • Juliana Veiga
    Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), State University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Alvaro C D Faria
    Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), State University of Rio de Janeiro, Rio de Janeiro, Brazil.
  • Pedro L Melo
    Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), State University of Rio de Janeiro, Rio de Janeiro, Brazil. Electronic address: plopes@uerj.br.

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