Automated detection of heart valve disorders with time-frequency and deep features on PCG signals

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dc.authorscopusid57203165669
dc.contributor.authorArslan, Özkan
dc.date.accessioned2023-04-20T08:02:24Z
dc.date.available2023-04-20T08:02:24Z
dc.date.issued2022
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Elektronik ve Haberleşme Mühendisliği Bölümü
dc.description.abstractHeart valve diseases (HVDs) can cause cardiac arrhythmias, heart attacks, and sudden cardiac death if not diagnosed early. Therefore, the detection of HVDs is critical in order to avoid heart-related mortality. The focus of this research is to establish an efficient computer-aided diagnosis approach that detects HVDs using phono-cardiogram (PCG) signals. The proposed approach uses traditional time-frequency and deep features with ma-chine learning models. The time-frequency features are extracted from non-linear measurements using discrete wavelet transform (DWT), wavelet packet transform (WPT), perceptual wavelet packet transform (PWPT) and empirical mode decomposition (EMD) methods. Deep features are extracted from VGG16, ResNet50 and MobileNetV2 pre-trained CNN models, and multilayer extreme learning machine (ML-ELM) model using scalo-gram images of PCG signals. Recursive feature elimination (RFE) algorithm is applied to all features and the most distinctive features are selected. Experimental results show that the PWPT + EMD features selected by RFE and the random forest (RF) classification model achieve the highest performance with accuracy of 99.4%, Matthews correlation coefficient (MCC) and G-mean of 99.3%. In another proposed approach, ML-ELM deep features selected by RFE algorithm and RF classification model provide accuracy and G-mean of 98.9%, and MCC values of 98.6%. It was observed that the time-frequency features have outperformed compared to deep features for the detection of HVDs. The proposed approach is compared with the existing studies and it has obtained higher performance values than the approaches using the same database. The proposed approach can be considered as an easily integrated system on the embedded platform.
dc.identifier.doi10.1016/j.bspc.2022.103929
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85132753161
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.103929
dc.identifier.urihttps://hdl.handle.net/20.500.11776/10902
dc.identifier.volume78
dc.identifier.wosWOS:000827248900003
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArslan, Özkan
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectHeart Valve Diseases (Hvds)
dc.subjectPhonocardiography (Pcg)
dc.subjectTime-Frequency Analysis
dc.subjectDeep Features
dc.subjectMultilayer Extreme Learning Machine
dc.subjectSound Classification
dc.subjectWavelet Transform
dc.subjectDiagnosis
dc.subjectEnsemble
dc.subjectDiseases
dc.subjectMachine
dc.titleAutomated detection of heart valve disorders with time-frequency and deep features on PCG signals
dc.typeArticle

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