Effect of Hilbert-Huang transform on classification of PCG signals using machine learning

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dc.authorscopusid57203165669
dc.authorscopusid57222328863
dc.contributor.authorArslan, Özkan
dc.contributor.authorKarhan, Mustafa
dc.date.accessioned2022-05-11T14:26:32Z
dc.date.available2022-05-11T14:26:32Z
dc.date.issued2022
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Elektronik ve Haberleşme Mühendisliği Bölümü
dc.description.abstractHeartbeat sounds are biological signals used in the early diagnosis of cardiovascular diseases. Digital heartbeat sound recordings, called phonocardiogram (PCG), are used in the determination and automatic classification of possible heart diseases. Healthy and pathological PCG signals are non-stationary signals and conventional feature extraction methods are insufficient in classifying these signals. In this study, PCG signals in healthy and four pathological categories are decomposed into intrinsic mode functions (IMFs) by Hilbert-Huang transform. Mel-frequency cepstral coefficient (MFCC) features were extracted from each mode to investigate the effect of the modes obtained by Hilbert-Huang transform on the classification of PCG signals. Genetic algorithm was used as feature selection method and k-nearest neighbor (KNN), multilayer perceptron (MLP), support vector machine (SVM) and deep neural network (DNN) machine learning methods were used as classifier. We have implemented multi classifications of five PCG classes (healthy, aortic stenosis, mitral stenosis, mitral regurgitation and mitral valve prolapse) by using 5-fold cross validation and 10 × 5-fold cross validation Data Analysis Protocol (DAP) framework. The results show that the DNN model provides the highest classification performance with 98.9% precision, 98.7% recall, 98.8% F1-score and 98.9% accuracy using 5-fold cross validation, and Matthews correlation coefficient of 0.981 using the DAP method. © 2021 The Authors
dc.identifier.doi10.1016/j.jksuci.2021.12.019
dc.identifier.issn1319-1578
dc.identifier.scopus2-s2.0-85123615506
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jksuci.2021.12.019
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6461
dc.identifier.wosWOS:000999620800063
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArslan, Özkan
dc.language.isoen
dc.publisherKing Saud bin Abdulaziz University
dc.relation.ispartofJournal of King Saud University - Computer and Information Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep neural network
dc.subjectEmpirical mode decomposition
dc.subjectGenetic algorithm
dc.subjectPhonocardiogram
dc.subjectTime-frequency analysis
dc.titleEffect of Hilbert-Huang transform on classification of PCG signals using machine learning
dc.typeArticle

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