Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 6, Issue 4, Pages 649-672
Research Article

Automatic Wheezing Detection Based on Signal Processing of Spectrogram and Back-Propagation Neural Network

Bor-Shing Lin,1 Huey-Dong Wu,2 and Sao-Jie Chen3

1Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan
2Department of Integrated Diagnostics and Therapeutics, National Taiwan University Hospital, Taipei, Taiwan
3Department and Graduate Institute of Electrical Engineering, National Taiwan University, Taipei, Taiwan

Received 1 April 2015; Accepted 1 August 2015

Copyright © 2015 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Wheezing is a common clinical symptom in patients with obstructive pulmonary diseases such as asthma. Automatic wheezing detection offers an objective and accurate means for identifying wheezing lung sounds, helping physicians in the diagnosis, long-term auscultation, and analysis of a patient with obstructive pulmonary disease. This paper describes the design of a fast and high-performance wheeze recognition system. A wheezing detection algorithm based on the order truncate average method and a back-propagation neural network (BPNN) is proposed. Some features are extracted from processed spectra to train a BPNN, and subsequently, test samples are analyzed by the trained BPNN to determine whether they are wheezing sounds. The respiratory sounds of 58 volunteers (32 asthmatic and 26 healthy adults) were recorded for training and testing. Experimental results of a qualitative analysis of wheeze recognition showed a high sensitivity of 0.946 and a high specificity of 1.0.