Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques

Jinglong Niu, Yan Shi, Maolin Cai, Zhixin Cao, Dandan Wang, Zhaozhi Zhang, Xiaohua Douglas Zhang

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Motivation Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%.

Original languageEnglish
Pages (from-to)820-827
Number of pages8
JournalBioinformatics
Volume34
Issue number5
DOIs
StatePublished - Mar 1 2018

Bibliographical note

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

Publisher Copyright:
© The Author 2017. Published by Oxford University Press. All rights reserved.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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