Int J Biol Sci 2018; 14(8):938-945. doi:10.7150/ijbs.23855 This issue

Research Paper

Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform

Yan Shi1,2,3*, Guoliang Wang4*, Jinglong Niu1✉, Qimin Zhang1, Maolin Cai1, Baoqing Sun5, Dandan Wang6, Mei Xue3, Xiaohua Douglas Zhang6✉

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2. The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China;
3. Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China;
4. Department of Electrical and Control Engineering, Beijing Union University, Beijing, China;
5. State Key Laboratory of Respiratory Disease, the 1st Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
6. Faculty of Health Sciences, University of Macau, Taipa, Macau.
*These authors contributed equally.

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license ( See for full terms and conditions.
Shi Y, Wang G, Niu J, Zhang Q, Cai M, Sun B, Wang D, Xue M, Zhang XD. Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform. Int J Biol Sci 2018; 14(8):938-945. doi:10.7150/ijbs.23855. Available from

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Graphic abstract

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.

Keywords: Respiratory system diagnosis, Auscultation, Sputum sound analysis, Discrete wavelet transform, Artificial neural network