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dc.contributor.authorAksahin, Mehmet Feyzi
dc.contributor.authorOltu, Burcu
dc.contributor.authorKaraca, Busra Kubra
dc.date.accessioned2021-06-30T13:16:01Z
dc.date.available2021-06-30T13:16:01Z
dc.date.issued2020
dc.identifier.issn1300-1884en_US
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/839650
dc.identifier.urihttp://hdl.handle.net/11727/6199
dc.description.abstractThe second leading cause of death in the world is cardiovascular diseases. Diagnosis of vast majority of cardiovascular diseases is made by listening to heart sounds by specialists (auscultation method). However, since the method of auscultation depends on the experience and hearing ability of the specialist, obtained results can be subjective. Therefore, digitization and visualization of heart sounds enables accurate, rapid and economical diagnosis of cardiovascular diseases, especially heart valve diseases. For this purpose, a device prototype that collects the heart sound from human body and also amplifies, filters, displays and records collected data on digital environment was designed in the first part of this study. In order to test the working accuracy of the designed device, clinical applications were carried out with the permission of the ethics committee and as the result of this application 15 heart sound recordings from 5 different disease groups(mitral insufficiency, mitral-aortic insufficiency, mitral-tricuspid insufficiency, mitral-aortic tricuspid insufficiency and healthy heart sound recordings) were collected.and obtained recordings were examined. The most effective parameter for the diagnosis of heart valve diseases is the location of the S1-S2 heart sounds. For this reason, in the second part of the study, a medical decision support system was established to detect the S1-S2 locations to assist physicians in their diagnosis. In this context, heart sounds are first filtered by discrete wavelet transform. Then, the S1-S2 waves in the filtered signal are made evident by the teager energy operator and rule-based algorithm. As a result, S1-S2 locations in normal and pathological data were detected with 98.67% sensitivity, 97.69% specificity and 98.18% accuracy.en_US
dc.language.isoturen_US
dc.relation.isversionof10.17341/gazimmfd.438614en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHeart sound recording deviceen_US
dc.subjectS1-S2 detectionen_US
dc.subjectteager energy operatoren_US
dc.subjectdiscrete wavelet transformen_US
dc.titleHeart sound recording and automatic S1-S2 waves detecting system designen_US
dc.typearticleen_US
dc.relation.journalJOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITYen_US
dc.identifier.volume35en_US
dc.identifier.issue1en_US
dc.identifier.startpage61en_US
dc.identifier.endpage70en_US
dc.identifier.wos000520598100006en_US
dc.identifier.scopus2-s2.0-85081963646en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergien_US


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