Publication:
Graph convolutional network-based deep feature learning for cardiovascular disease recognition from heart sound signals

dc.contributor.authorRezaee, K.
dc.contributor.authorKhosravi, M. R.
dc.contributor.authorJabari, M.
dc.contributor.authorHesari, S.
dc.contributor.authorAnari, M. S.
dc.contributor.authorHosseinabadi, Fahimeh Aghaei
dc.contributor.ozugradstudentHosseinabadi, Fahimeh Aghaei
dc.date.accessioned2023-06-08T07:19:23Z
dc.date.available2023-06-08T07:19:23Z
dc.date.issued2022-12
dc.description.abstractThe high mortality rate and prevalence of cardiovascular disease (CVD) make early detection of the disease essential. Due to its simplicity and low cost, the phonocardiogram (PCG) system is widely used in healthcare applications for the recognition of CVD in multiclass problems. On the basis of the PCG signal, this paper proposes a hybrid method for classifying cardiac sounds with deep extracted features through two-step learning. For fine-grained features in Graph Convolutional Networks (GCNs), sampling and prior layers are employed. A PCG signal is divided into equal parts with overlap using the windowing process. L-spectrograms extract frequency-domain information from signals to figure out their power spectrum. Furthermore, the deep GCN tries to determine the association between CVD and spectrogram images to recognize CVD signals better. Combining retrieved features with convolutional neural network (CNN) characteristics reveals an image's intrinsic associations. To generate relational feature representations, correlations between clusters and GCN are visualized using a graph structure. CNN's discriminative ability has been enhanced by incorporating GCN attributes. Using Michigan Heart Sound and Murmur Database and PhysioNet/CinC 2016 Challenge results, we are 99.44% and 96.16% accurate, respectively. Through a combination of GCN architecture, CNN design, and deep features, the hybrid model significantly improves CVD classification accuracy. Measuring metrics demonstrate that the proposed approach detects CVD more effectively than previous approaches.en_US
dc.identifier.doi10.1002/int.23041en_US
dc.identifier.endpage11274en_US
dc.identifier.issn0884-8173en_US
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85137235727
dc.identifier.startpage11250en_US
dc.identifier.urihttp://hdl.handle.net/10679/8361
dc.identifier.urihttps://doi.org/10.1002/int.23041
dc.identifier.volume37en_US
dc.identifier.wos000848728800001
dc.language.isoengen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.publisherWileyen_US
dc.relation.ispartofInternational Journal of Intelligent Systems
dc.relation.publicationcategoryInternational Refereed Journal
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsCVD classificationen_US
dc.subject.keywordsDecision support systemen_US
dc.subject.keywordsDeep learningen_US
dc.subject.keywordsGraph convolutional networksen_US
dc.subject.keywordsPhonocardiogramen_US
dc.subject.keywordsSpectrogramen_US
dc.titleGraph convolutional network-based deep feature learning for cardiovascular disease recognition from heart sound signalsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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