Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model

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dc.contributor.authorYildirim, Muhammed
dc.date.accessioned2026-06-19T06:41:19Z
dc.date.available2026-06-19T06:41:19Z
dc.date.issued2022
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractHeart sounds have been widely used for years to monitor and classify heart diseases. Experts manually examine these sounds, which is arduous and time-consuming. In addition, since interpreting these sounds requires experience, experts who do not have enough experience may misinterpret these sounds. For this reason, a new deep one-dimensional Convolutional Neural Network (1D-CNN) architecture has been proposed to increase the detection accuracy and alleviate the workload of experts in the classification of sound signals used in the diagnosis of heart valve diseases. In the developed model, first, feature maps were obtained from heart sounds by using the MFCC method. High performance was achieved when the feature maps obtained later were classified in the developed deep architecture. Furthermore, the feature maps generated by the MFCC approach were classified using traditional machine learning classifiers. When the obtained results were compared, it was observed that the suggested deep model was more successful. In the developed architecture, an accuracy rate of 99.5% was obtained. The accuracy rate obtained shows that the developed architecture can be used to classify heart sounds and diagnose heart valve diseases.
dc.identifier.doi10.1002/cpe.7232
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue24
dc.identifier.orcid0000-0003-1866-4721
dc.identifier.scopus2-s2.0-85134673068
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/cpe.7232
dc.identifier.urihttps://hdl.handle.net/20.500.12899/6186
dc.identifier.volume34
dc.identifier.wosWOS:000829755800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorYildirim, Muhammed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofConcurrency and Computation-Practice & Experience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectArtificial Intelligence
dc.subjectCardiac Disorder
dc.subjectClassification
dc.subjectHeart Sound
dc.subjectMfcc
dc.titleAutomatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model
dc.typeArticle

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