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A hybrid particle swarm optimization-SVM classificationfor automatic cardiac auscultation |
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| รหัสดีโอไอ | |
| Creator | 1. Prasertsak Charoen 2. Waree Kongprawechnon 3. Kanokvate Tungpimolrut |
| Title | A hybrid particle swarm optimization-SVM classificationfor automatic cardiac auscultation |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2560 |
| Journal Title | Songklanakarin Journal of Science and Technology (SJST) |
| Journal Vol. | 39 |
| Journal No. | 2 |
| Page no. | 171 |
| Keyword | support vector machines,cardiac auscultation,particle swarm optimization,machine learning |
| ISSN | 0125-3395 |
| Abstract | Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the conditionof the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditionswithout need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM)and particle swarm optimization (PSO) for an automatic cardiac auscultation system. The model consists of two parts: heartsound signal processing part and a proposed PSO for weighted SVM (WSVM) classifier part. In this method, the PSO takesinto account the degree of importance for each feature extracted from wavelet packet (WP) decomposition. Then, by usingprinciple component analysis (PCA), the features can be selected. The PSO technique is used to assign diverse weights todifferent features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM modelsachieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs), compared totraditional SVM and genetic algorithm (GA) based SVM. |
