A lung sound classification system based on the rational dilation wavelet transform

Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug:2016:3745-3748. doi: 10.1109/EMBC.2016.7591542.


In this work, a wavelet based classification system that aims to discriminate crackle, normal and wheeze lung sounds is presented. While the previous works related with this problem use constant low Q-factor wavelets, which have limited frequency resolution and can not cope with oscillatory signals, in the proposed system, the Rational Dilation Wavelet Transform, whose Q-factors can be tuned, is employed. Proposed system yields an accuracy of 95 % for crackle, 97 % for wheeze, 93.50 % for normal and 95.17 % for total sound signal types using energy feature subset and proposed approach is superior to conventional low Q-factor wavelet analysis.

MeSH terms

  • Algorithms
  • Databases, Factual
  • Humans
  • Respiratory Sounds / physiology*
  • Signal Processing, Computer-Assisted*
  • Wavelet Analysis