ECG beat classification algorithm based on cluster analysis

doi:10.11772/j.issn.1001-9081.2014.07.2132

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (7): 2132-2135.DOI: 10.11772/j.issn.1001-9081.2014.07.2132

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ECG beat classification algorithm based on cluster analysis

YAN Yu¹,SUN Cheng²

1. Information Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China;
2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Guangdong 518055, China

Received:2014-01-26 Revised:2014-03-04 Online:2014-08-01 Published:2014-07-01
Contact: YAN Yu

基于聚类分析的心电节拍分类算法

鄢羽¹,孙成²

1. 重庆医科大学附属第一医院信息中心，重庆 400016
2. 中国科学院深圳先进技术研究院，广东深圳 518055

通讯作者: 鄢羽
作者简介:鄢羽(1988-)，女，重庆人，硕士，主要研究方向：数据挖掘、智慧医疗；孙成(1988-)，男，广东深圳人，硕士，主要研究方向：模式识别、智慧医疗。
基金资助:
广东省与中国科学院全面战略合作计划项目

Abstract

Abstract:

In order to improve the accuracy and universality of computer-assisted classification algorithm, a Electrocardiography (ECG) beat classification algorithm based on cluster analysis was presented in this paper. The algorithm considered that one patients' ECG beats repeated periodically, and used the method of two-stage cluster analysis, and selecting representative ECG beats, combined with the diagnosis of cardiac physicians to achieve accurate ECG beat classification rate. In order to verify the accuracy of the algorithm, using the internationally standard database MIT-BIH arrhythmia database, the ECG beat classification method and the accuracy evaluation method specified by AAMI/ANSI standard were used to perform simulation experiments, the final overall classification accuracy rate is 99.07%. Compared with Kiranyaz' method(KIRANYAZ S, INCE T,PULKKINEN J, et al. Personalized long-term ECG classification: A systematic approach［J］. Expert Systems with Applications, 2011, 38(4): 3220-3226.), this method does not require specific training step, and the sensitivity of the ECG beats which labeled as S raise to 89.82% from 40.15%, significantly improving classification algorithm's generalization capability.

摘要：

为提高计算机辅助心电节拍分类算法的准确率和普适性，提出一种基于聚类分析的心电节拍分类算法，该算法利用心电节拍个体内差异性较小的特性，采用两级聚类分析、抽样代表性心电节拍的方法，结合心电医师的辅助诊断，实现对心电节拍的准确分类。为了验证算法的准确性，采用国际公认的标准数据库——MIT-BIH心律失常数据库,AAMI/ANSI标准规定的心电节拍分类方法及准确率的计算方法进行仿真实验，最终总体分类准确率达到99.07%。与Kiranyaz等(KIRANYAZ S, INCE T,PULKKINEN J, et al. Personalized long-term ECG classification: A systematic approach［J］. Expert Systems with Applications, 2011, 38(4): 3220-3226.)的心电节拍分类算法相比，该算法无需进行设定的训练，且S类心电节拍分类灵敏度由40.15%提高到89.82%，显著提高了分类算法的普适性。

CLC Number:

TP18

YAN Yu SUN Cheng. ECG beat classification algorithm based on cluster analysis[J]. Journal of Computer Applications, 2014, 34(7): 2132-2135.

鄢羽孙成. 基于聚类分析的心电节拍分类算法[J]. 计算机应用, 2014, 34(7): 2132-2135.

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ECG beat classification algorithm based on cluster analysis

基于聚类分析的心电节拍分类算法

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