Using of the machine learning methods to identify bronchopulmonary system diseases with the use of lung sounds

Authors

  • M. G. Chekhovych National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Ukraine
  • A. S. Poreva National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Ukraine
  • V. I. Timofeyev National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Ukraine
  • P. Henaff Université de Lorraine, France

DOI:

https://doi.org/10.20535/RADAP.2018.73.55-62

Keywords:

electronic stethoscope, lung sound, spectral analysis, wavelet transform, machine learning

Abstract

This study reviews the main approaches to the analyzing of modern methods of digital processing of lung sounds. It is shown that each of the existing methods gives a definite result in solving a particular problem. However, none of the methods that were reviewed, can’t be called universal and completely convenient for using in the real conditions of the hospital. Certain numerical parameters can be obtained, as a result of the work of each method. In this study it is showed that machine learning can serve as a unifying mechanism for the considered methods. A set of different parameters can be the input arguments of the classifier, which will be properly trained. As a result, the primary opinion in a convenient and accessible form can be presented to the doctor.

Author Biographies

M. G. Chekhovych, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

Chekhovych M. G., BS

A. S. Poreva, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

Poreva A. S.,  Senior lecturer

V. I. Timofeyev, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

Timofeyev V. I., Doctor of Science (Techn.), Professor of the Electronic Engineering Department

P. Henaff, Université de Lorraine

Henaff Patrick, Professor at Ecole des Mines de Nancy, Information and Systems Department, (Campus ARTEM)

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Published

2018-06-30

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