Heart sound signal classification using fast independent component analysis
| dc.contributor.author | Koçyiǧit Y. | |
| dc.date.accessioned | 2025-04-10T11:09:36Z | |
| dc.date.available | 2025-04-10T11:09:36Z | |
| dc.date.issued | 2016 | |
| 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%. © 2016 Tübitak. | |
| dc.identifier.DOI-ID | 10.3906/elk-1409-123 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/48873 | |
| dc.publisher | Turkiye Klinikleri Journal of Medical Sciences | |
| dc.title | Heart sound signal classification using fast independent component analysis | |
| dc.type | Article |