Heart sound signal classification using fast independent component analysis
| dc.contributor.author | Koçyigit, Y | |
| dc.date.accessioned | 2025-04-10T10:25:54Z | |
| dc.date.available | 2025-04-10T10:25:54Z | |
| dc.description.abstract | The 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.e-issn | 1303-6203 | |
| dc.identifier.issn | 1300-0632 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/33645 | |
| dc.language.iso | English | |
| dc.title | Heart sound signal classification using fast independent component analysis | |
| dc.type | Article |