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Automatic wheezing detection offers an objective and accurate means for identifying wheezing lung sounds, helping physicians in the diagnosis, long\u2010term auscultation, and analysis of a patient with obstructive pulmonary disease. This paper describes the design of a fast and high\u2010performance wheeze recognition system. A wheezing detection algorithm based on the order truncate average method and a back\u2010propagation 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.<\/jats:p>","DOI":"10.1260\/2040-2295.6.4.649","type":"journal-article","created":{"date-parts":[[2015,12,18]],"date-time":"2015-12-18T12:58:46Z","timestamp":1450443526000},"page":"649-672","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Automatic Wheezing Detection Based on Signal Processing of Spectrogram and Back\u2010Propagation Neural Network"],"prefix":"10.1155","volume":"6","author":[{"given":"Bor-Shing","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huey-Dong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sao-Jie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2015,12,18]]},"reference":[{"key":"e_1_2_1_1_2","unstructured":"Centers for Disease Control and Prevention (CDC). 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