{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T03:51:33Z","timestamp":1754020293709},"reference-count":11,"publisher":"Georg Thieme Verlag KG","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Methods Inf Med"],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on \u201cBiosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems\u201d.<\/jats:p><jats:p>Objectives: This work proposes an algorithm for diagnostic classification of multi-channel respiratory sounds.<\/jats:p><jats:p>Methods: 14-channel respiratory sounds are modeled assuming a 250-point second order vector autoregressive (VAR) process, and the estimated model parameters are used to feed a support vector machine (SVM) classifier. Both a three-class classifier (healthy, bronchi ectasis and interstitial pulmonary disease) and a binary classifier (healthy versus pathological) are considered.<\/jats:p><jats:p>Results: In the binary scheme, the sensitivity and specificity for both classes are 85% \u00b1 8.2%. In the three-class classification scheme, the healthy recall (95% \u00b1 5%) and the interstitial pulmonary disease recall and precision (100% \u00b1 0% both) are rather high. However, bronchiectasis recall is very low (30% \u00b1 15.3%), resulting in poor healthy and bronchiectasis precision rates (76% \u00b1 8.7% and 75% \u00b1 25%, respectively). The main reason behind these poor rates is that the bronchiectasis is confused with the healthy case.<\/jats:p><jats:p>Conclusions: The proposed method is promising, nevertheless, it should be improved such that other mathematical models, additional features, and\/or other classifiers are to be experimented in future studies.<\/jats:p>","DOI":"10.3414\/me13-02-0041","type":"journal-article","created":{"date-parts":[[2014,7,4]],"date-time":"2014-07-04T08:41:11Z","timestamp":1404463271000},"page":"291-295","source":"Crossref","is-referenced-by-count":9,"title":["Computerized Diagnosis of Respira tory Disorders"],"prefix":"10.3414","volume":"53","author":[{"given":"M.","family":"Saraclar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y. P.","family":"Kahya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"I.","family":"Sen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2018,1,20]]},"reference":[{"key":"10.3414\/ME13-02-0041-1","doi-asserted-by":"publisher","DOI":"10.3414\/ME12-01-0037"},{"key":"10.3414\/ME13-02-0041-2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-010-0663-5"},{"key":"10.3414\/ME13-02-0041-3","doi-asserted-by":"publisher","DOI":"10.1016\/0010-4825(94)90038-8"},{"key":"10.3414\/ME13-02-0041-4","doi-asserted-by":"crossref","unstructured":"Ciftci K, Kahya YP. Respiratory airflow estimation by time varying autoregressive modeling. 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Cambridge, MA: The MIT Press; 2010"},{"key":"10.3414\/ME13-02-0041-10","unstructured":"Duda RO, Hart PE, Stork DG. Pattern Classification, 2nd Edition. Wiley; 2000"},{"key":"10.3414\/ME13-02-0041-11","doi-asserted-by":"crossref","unstructured":"Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011; 2 (3): 27: 1-27: 27. Software available at http:\/\/www.csie.ntu.edu.tw\/ cjlin\/libsvm","DOI":"10.1145\/1961189.1961199"}],"container-title":["Methods of Information in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.3414\/ME13-02-0041.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T11:43:33Z","timestamp":1565610213000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.3414\/ME13-02-0041"}},"subtitle":["SVM Based Classification of VAR Model Parameters of Respiratory Sounds"],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":11,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2018,1,20]]},"published-print":{"date-parts":[[2014]]}},"URL":"https:\/\/doi.org\/10.3414\/me13-02-0041","relation":{},"ISSN":["0026-1270","2511-705X"],"issn-type":[{"value":"0026-1270","type":"print"},{"value":"2511-705X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014]]}}}