期刊
JOURNAL OF HEALTHCARE ENGINEERING
卷 6, 期 4, 页码 649-672出版社
HINDAWI LTD
DOI: 10.1260/2040-2295.6.4.649
关键词
Asthma; wheezing detection; bilateral filtering; order truncate average; back-propagation neural network
资金
- Ministry of Science and Technology in Taiwan (R. O. C.) [MOST 103-2218-E-305-001, MOST 103-2218-E-305-003, MOST 104-2221-E-305-006]
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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