基于聚类分析的心电节拍分类算法

doi:10.11772/j.issn.1001-9081.2014.07.2132

计算机应用 ›› 2014, Vol. 34 ›› Issue (7): 2132-2135.DOI: 10.11772/j.issn.1001-9081.2014.07.2132

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

收稿日期:2014-01-26 修回日期:2014-03-04 出版日期:2014-07-01 发布日期:2014-08-01
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

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:2014-07-01 Published:2014-08-01
Contact: YAN Yu

摘要/Abstract

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。

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.

中图分类号:

TP18

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.

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

参考文献

[1]OSOWSKI S, LINH T. ECG beat recognition using fuzzy hybrid neural network［J］. IEEE Transactions on Biomedical Engineering, 2001, 48(11): 1265-1271.
[2]YU S, CHEN Y. Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network［J］. Pattern Recognition Letters, 2007, 28(10): 1142-1150.
[3]HU Y, TOMPKINS W, URRUSTI J L, et al. Applications of artificial neural networks for ECG signal detection and classification［J］. Journal of Electrocardiology, 1993, 26(S1): 66-73.
[4]HU Y, PALREDDY S, TOMPKINS J. A patient-adaptable ECG beat classifier using a mixture of experts approach［J］. IEEE Transactions on Biomedical Engineering, 1997, 44(9): 891-900.
[5]DECHAZAL P, O'DWYER M, REILLY R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features［J］. IEEE Transactions on Biomedical Engineering, 2004, 51(7): 1196-1206.
[6]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.
[7]LIN Z, GE Y, TAO G. Algorithm for clustering analysis of ECG data［C］// Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2006: 3857-3860.
[8]DELGADO E, RODRIGUEZ J, JIMENEZ F. Recognition of cardiac arrhythmias by means of beat clustering on ECG-holter records［C］// Computers in Cardiology 2007. Piscataway: IEEE, 2007: 161-164.
[9]PhysioBank Archive Index［EB/OL］.［2013-10-20］.http://physionet.org/physiobank/database/.
[10]SONG X, DENG Q. On the format of MIT-BIH arrhythmia database［J］. Chinese Journal of Medical Physics, 2004, 21(4): 230-232.(宋喜国, 邓亲恺. MIT-BIH 心率失常数据库的识读及应用［J］. 中国医学物理学杂志, 2004, 21(4): 230-232.)
[11]Association for the Advancement of Medical Instrumentation. ANSI/AAMI EC57:2012, Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms［S］. Arlington: Association for the Advancement of Medical Instrumentation, 2012: 1-22.
[12]SHENSA M. The discrete wavelet transform: wedding the a trous and Mallat algorithms［J］. IEEE Transactions on Signal Processing, 1992, 40(10): 2464-2482.
[13]PRTools［EB/OL］.［2013-10-20］.http://prtools.org/.
[14]LIU X, YAN C, HU Z. Arrhythmia classification based on mathematical morphology and support vector machine［J］. Journal of Computer Applications, 2013, 33(4): 1173-1175.(刘雄飞, 晏晨伟, 胡志坤. 基于数学形态学及支持向量机的心率失常识别［J］. 计算机应用, 2013, 33(4):1173-1175.)
[15]TONG J, DONG J. Electrocardiogram classification using combined classifiers［J］. Journal of Computer Applications, 2010, 30(4): 1225-1228.(童佳斐, 董军. 分类器组合在心电图分类中的应用［J］. 计算机应用, 2010, 30(4):1225-1228.)

基于聚类分析的心电节拍分类算法

ECG beat classification algorithm based on cluster analysis

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	杜航原郝思聪王文剑. 结合图自编码器与聚类的半监督表示学习方法[J]. 计算机应用, 0, (): 0-0.
[2]	陈露张晓霞于洪. 基于先验知识的非负矩阵半可解释三因子分解算法[J]. 计算机应用, 0, (): 0-0.
[3]	韩舒宁徐敏董学士林青沈凡凡. 混合伊藤算法求解多尺度着色旅行商问题[J]. 计算机应用, 0, (): 0-0.
[4]	李晓杰崔超然宋广乐苏雅茜吴天泽张春云. 基于时序超图卷积神经网络的股票趋势预测方法[J]. 计算机应用, 0, (): 0-0.
[5]	张建严珂马祥. 基于神经网络的复杂垃圾信息过滤算法分析[J]. 计算机应用, 0, (): 0-0.
[6]	邱云志汪廷华戴小路. 双重特征加权模糊支持向量机[J]. 计算机应用, 0, (): 0-0.
[7]	李宗正周恺卿丁雷欧云. 基于基因交换的自适应人工鱼群算法[J]. 计算机应用, 0, (): 0-0.
[8]	刘清华廖士中. 基于随机素描方法的在线核回归[J]. 计算机应用, 0, (): 0-0.
[9]	张小清王晨曦吕彦林耀进. 基于ReliefF的层次分类在线流特征选择算法[J]. 计算机应用, 0, (): 0-0.
[10]	于婉莹梁美玉王笑笑陈徵曹晓雯. 基于深度注意力网络的课堂教学视频中学生表情识别与智能教学评估[J]. 计算机应用, 0, (): 0-0.
[11]	黄勇康梁美玉王笑笑陈徵曹晓雯. 基于深度时空残差卷积神经网络的课堂教学视频中多人课堂行为识别[J]. 计算机应用, 0, (): 0-0.
[12]	康猛蒙祖强. 基于局部条件区分能力的高效属性约简算法[J]. 计算机应用, 0, (): 0-0.
[13]	谢鑫张贤勇王旋晔唐鹏飞. 变精度邻域等价粒邻域决策树构造算法[J]. 计算机应用, 0, (): 0-0.
[14]	刘忠慧王梓宥闵帆. 近似概念的遗传生成算法及其推荐应用[J]. 计算机应用, 0, (): 0-0.
[15]	潘仁志钱付兰赵姝张燕平. 基于卷积神经网络交互的用户属性偏好建模的推荐模型[J]. 计算机应用, 0, (): 0-0.