Classification of sputum sounds using artificial neural network and wavelet transform

Yan Shi, Guoliang Wang, Jinglong Niu, Qimin Zhang, Maolin Cai, Baoqing Sun, Dandan Wang, Mei Xue, Xiaohua Douglas Zhang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensive care unit (ICU) staff and achieve timely clearance of secretion in patients with mechanical ventilation, we propose a method consisting of feature extraction of sputum sound signals using the wavelet transform and classification of sputum existence using artificial neural network (ANN). Sputum sound signals were decomposed into the frequency subbands using the wavelet transform. A set of features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN system, trained using the Back Propagation (BP) algorithm, was implemented to recognize the existence of sputum sounds. The maximum precision rate of automatic recognition in texture of signals was as high as 84.53%. This study can be referred to as the optimization of performance and design in the automatic technology for sputum detection using sputum sound signals.

Original languageEnglish
Pages (from-to)938-945
Number of pages8
JournalInternational Journal of Biological Sciences
Issue number8
StatePublished - May 22 2018

Bibliographical note

Funding Information:
This work was supported by the Start-up Research Grant (SRG2016-00083-FHS) at University of Macau, the National Natural Science Foundation of China (Grant No. 51575020) and open foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems.

Publisher Copyright:
© Ivyspring International Publisher.


  • Artificial neural network
  • Auscultation
  • Discrete wavelet transform
  • Respiratory system diagnosis
  • Sputum sound analysis

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Applied Microbiology and Biotechnology
  • Molecular Biology
  • Developmental Biology
  • Cell Biology


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