Koçyiǧit Y.2024-07-222024-07-22201613000632http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/16047The 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%. © 2016 Tübitak.EnglishAll Open Access; Bronze Open AccessBiomedical signal processingCardiologyClassifiersDiscriminant analysisHeartIndependent component analysisMathematical transformationsSupport vector machinesWavelet transforms10-fold cross-validationClinical informationFast independent component analysisHeart sound signalHeart soundsIndependent component analysis(ICA)Linear discriminant analysisNaive bayesPrincipal component analysisArticle10.3906/elk-1409-123