Abnormal Heart Sound Detection Using Ensemble Classifiers

dc.contributor.authorZan, Hasan
dc.contributor.authorYıldız, Abdulnasır
dc.date.accessioned2019-06-26T12:44:33Z
dc.date.available2019-06-26T12:44:33Z
dc.date.issued2018
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractPhonocardiogram is used for ambulatory diagnostic to assess health status of heart and detect cardiovascular disease. The goal of this study is to develop automatic classification method of PCG recordings collected from different databases and recorded in a different way. For this purpose, after various time and frequency domain features are extracted from PCG recordings obtained from two databases, recordings are subjected to pre-classification in order determine which database they are obtained from. Before final classification, various time, frequency and time-frequency domain features of classified recordings are extracted. These features are fed into four different classification ensembles trained with training dataset. With final decision rule, proposed algorithm achieved an accuracy of 98.9%, a sensitivity of 93.75% and a specify of 99.5%.en_US
dc.identifier.citationH. Zan and A. Yıldız, "Abnormal Heart Sound Detection Using Ensemble Classifiers," 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 2018, pp. 1-4.en_US
dc.identifier.isbn978-1-5386-6878-8
dc.identifier.urihttps://hdl.handle.net/20.500.12514/1006
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.publicationcategoryKategorisiz
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectResearch Subject Categories::TECHNOLOGY::Information technology::Signal processingen_US
dc.titleAbnormal Heart Sound Detection Using Ensemble Classifiersen_US
dc.typeArticleen_US

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