Arrhythmia Classification via k-Means based Polyhedral Conic Functions Algorithm

被引:0
作者
Cimen, Emre [1 ]
Ozturk, Gurkan [1 ]
机构
[1] Anadolu Univ, Ind Engn, Eskisehir, Turkey
来源
2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & COMPUTATIONAL INTELLIGENCE (CSCI) | 2016年
关键词
arrhythmia; classification; clustering; mathematical programming; HEARTBEAT INTERVAL FEATURES; ECG MORPHOLOGY; SEPARATION;
D O I
10.1109/CSCI.2016.154
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:798 / 802
页数:5
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