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

Lin, Bor-Shing; Wu, Huey-Dong; Chen, Sao-Jie

doi:https://doi.org/10.1260/2040-2295.6.4.649

Journal of Healthcare Engineering

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Research Article | Open Access

Volume 6 | Article ID 780103 | https://doi.org/10.1260/2040-2295.6.4.649

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

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

Received01 Apr 2015

Accepted01 Aug 2015

Abstract

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.

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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.

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