Neural classification of lung sounds using wavelet coefficients.

  • Kandaswamy A
  • Kumar C
  • Ramanathan R
 et al. 
  • 17

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.

Abstract

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.

Author-supplied keywords

  • Algorithms
  • Auscultation
  • Auscultation: statistics & numerical data
  • Humans
  • Neural Networks (Computer)
  • Respiratory Sounds
  • Respiratory Sounds: classification
  • Respiratory Sounds: physiology

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • a Kandaswamy

  • C S C Sathish Kumar

  • Rm Pl Ramanathan

  • S Jayaraman

  • N Malmurugan

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free