Heart sound signal classification using fast independent component analysis

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dc.contributor.authorKoçyigit, Y
dc.date.accessioned2024-07-18T11:39:49Z
dc.date.available2024-07-18T11:39:49Z
dc.description.abstractThe analysis of heart sound signals is a basic method for heart examination. It may indicate the presence of heart disorders and provide clinical information in the diagnostic process. In this study, a novel feature dimension reduction method based on independent component analysis (ICA) has been proposed for the classification of fourteen different heart sound types; the method was compared with principal component analysis. The feature vectors are classified by support vector machines, linear discriminant analysis, and naive Bayes (NB) classifiers using 10 -fold cross validation. The ICA combined with NB achieves the highest average performance with a sensitivity of 98.53%, specificity of 99.89%, g -means of 99.21%, and accuracy of 99.79%.
dc.identifier.issn1300-0632
dc.identifier.other1303-6203
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/1924
dc.language.isoEnglish
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.subjectHIDDEN MARKOV MODEL
dc.subjectWAVELET TRANSFORM
dc.subjectPCA
dc.subjectDIAGNOSIS
dc.subjectSYSTEM
dc.subjectICA
dc.subjectLDA
dc.subjectALGORITHMS
dc.subjectDISEASE
dc.titleHeart sound signal classification using fast independent component analysis
dc.typeArticle

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