Arrhythmia Classification via k-Means Based Polyhedral Conic Functions Algorithm
Arrhythmia Classification via k-Means Based Polyhedral Conic Functions Algorithm
Heart disease is one of the important cause of death. In this study, we used ECG data obtained from MIT-BIH database to classify arrhythmias. We select 5 classes, normal beat (N), right bundle branch block (RBBB), left bundle branch block (LBBB), atrial premature contraction (APC) and ventricular premature contraction (VPC). We applied k-means based Polyhedral Conic Functions (k-means PCF) algorithm to classify instances. The performance of the proposed classifier is shown with numerical experiments. With proposed algorithm we obtained 98 % accuracy rate. This test result is compared with other well known classification methods.
- Anadolu University Turkey
Mathematical Programming, Classification, Arrhythmia, Clustering
Mathematical Programming, Classification, Arrhythmia, Clustering
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