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.
|Number of pages
|International Journal of Biological Sciences
|Published - May 22 2018
Bibliographical noteFunding 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.
© Ivyspring International Publisher.
- Artificial neural network
- 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