基于改进K均值聚类生成匹配模板的心搏分类方法  

ECG beat classification method based on match template generated by improved K-means clustering

作　　者：陈永波 徐静波[2] 王云峰[2] 张海英[2] CHEN Yong-bo;XU Jing-bo;WANG Yun-feng;ZHANG Hai-ying(School of Microeleetronics, University of Chinese Academy of Sciences, Beijing 101047, China;Beijing Key Laboratory of Radio Frequency IC Technology for Next Generation Communications, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China)

机构地区：[1]中国科学院大学微电子学院,北京101047 [2]中国科学院微电子研究所新一代通信射频芯片技术实验室北京市重点实验室,北京100029

出　　处：《传感器与微系统》2018年第4期20-23,共4页Transducer and Microsystem Technologies

摘　　要：为提高分析含大量数据的动态心电时的准确性和分析效率,提出了一种基于改进的K均值聚类生成心搏模板的匹配方法。使用K均值聚类和波形反混淆技术进行循环纠错,生成可变宽心搏模板、并建立心搏模板库。利用可变宽心搏模板和相关系数相结合的策略,对动态心电中心搏进行快速准确分类。实验方法经心率失常数据库MIT-BIT和ANMA/ANSI标准验证,分类结果总体准确率达98.06%,达到了心搏分类目标。To improve accuracy and efficiency when analyze morphology of large dataset of dynamic electrocardiography（ECG）,a ECG beat classification method based on beat templates generated by improved Kmeans clustering is presented. It takes K-means clustering and DEMIX technology to correct errors circularly,generates variable-width beat templates and establish beat templates database. By using the strategy which combines variable-width beat templates with correlation coefficient,the method can classify the ECG beats efficiently and accurately. The experimental verification is evaluated on the MIT-BIT arrhythmia database and ANMA/ANSI standard,and the overall accuracy rate of classification result is 98. 06 %,it achieves the goal of beats classification.

关 键 词：动态心电 K均值聚类 波形反混淆 心搏模板 心搏分类 

分 类 号：R138[医药卫生—劳动卫生]