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dc.contributor.authorNguyen, Thanh-Nghia
dc.contributor.authorNguyen, Thanh-Hai
dc.contributor.authorNguyen, Manh-Hung
dc.contributor.authorLivatino, Salvatore
dc.date.accessioned2019-10-01T00:08:55Z
dc.date.available2019-10-01T00:08:55Z
dc.date.issued2019-09
dc.identifier.citationNguyen , T-N , Nguyen , T-H , Nguyen , M-H & Livatino , S 2019 , ' Wavelet-Based Kernel Construction for Heart Disease Classification ' , AEEE Advances in Electrical and Electronic Engineering , vol. 17 , no. 3 , pp. 306-319 . https://doi.org/10.15598/aeee.v17i3.3270
dc.identifier.issn1804-3119
dc.identifier.otherPURE: 17435213
dc.identifier.otherPURE UUID: 133cb724-6188-44d7-9812-505ad61cae3a
dc.identifier.otherScopus: 85069507272
dc.identifier.urihttp://hdl.handle.net/2299/21708
dc.description© 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING
dc.description.abstractHeart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.en
dc.format.extent14
dc.language.isoeng
dc.relation.ispartofAEEE Advances in Electrical and Electronic Engineering
dc.rightsOpen
dc.subjectBack-propagation neural network
dc.subjectElectrocardiogram signals
dc.subjectHeart disease classification
dc.subjectWavelet coefficients
dc.subjectWavelet-based kernel principal component analysis
dc.subjectElectrical and Electronic Engineering
dc.titleWavelet-Based Kernel Construction for Heart Disease Classificationen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Engineering and Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85069507272&partnerID=8YFLogxK
dc.relation.schoolSchool of Engineering and Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2019-09
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.15598/aeee.v17i3.3270
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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