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A new type of wavelet de-noising algorithm for lung sound signals

  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the development of digital auscultation, the computer-based intelligent auscultation of respiratory sounds has drawn attention of researchers. However, the noises of acquired signals influence the further analysis of lung sound, so there is necessity to develop the noise reduction algorithm of lung sound signals. In this paper, a new type of noise reduction is proposed. The original signals are decomposed into 7 layers by wavelet transform. The locations of lung sound part are obtained in the sub-signals by the mean values of autocorrelation coefficients. The noises between lung sound parts are reduced by setting zero directly. The noises in the lung sound parts are filtered by a Chebyshev type I band-pass filter. The de-noising results are judged by two means. One is the subjective judgement of internal physicians and the de-noising effect is accepted by doctors without distortion. The other is the classification result of sound types in the further research by BP neural network and the classification accuracy can reach 85%.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2544-2548
Number of pages5
ISBN (Electronic)9781538654880
DOIs
StatePublished - 21 Jan 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/TerritorySpain
CityMadrid
Period3/12/186/12/18

Keywords

  • auto-correlation
  • lung sound part location
  • noise reduction
  • respiratory sound
  • wavelet transform

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