Ensemble of Support Vector Machines for Heartbeat Classification

摘要：

This paper proposes a novel and simple method that uses the randomness of random matrix and SVM ensemble learning to discriminate eight types of heartbeats. We use random matrices to generate 15 groups of random features. Then we construct one SVM classifier on each group of random features along with a RR interval. The type of heartbeat is determined using majority voting strategy to combine 15 SVM classifiers. 3062 heartbeats obtained from the MIH-BIH electrocardiogram (ECG) database are used for experiments. The results show that our proposed method has an accuracy of 98.65％ and is an effective method for heartbeat classification.

关键词： electrocardiogram (ECG) heartbeat classification arrhythmia ensemble classifier support vector machine (SVM) random matrix

作者: Huifang Huang Guangshu Hu LiZhu

作者单位: Department of Biomedical Engineering, Tsinghua University, Beijing, China Department of Biomedical E Department of Biomedical Engineering, Tsinghua University, Beijing, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 1327-1330

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）