Arrhythmia Classification via k-Means based Polyhedral Conic Functions Algorithm


International Conference on Computational Science and Computational Intelligence (CSIC), Nevada, United States Of America, 15 - 17 December 2016, pp.798-802 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/csci.2016.154
  • City: Nevada
  • Country: United States Of America
  • Page Numbers: pp.798-802
  • Keywords: arrhythmia, classification, clustering, mathematical programming, HEARTBEAT INTERVAL FEATURES, ECG MORPHOLOGY, SEPARATION
  • Anadolu University Affiliated: Yes


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.