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

CHEN Yong-bo; XU Jing-bo; WANG Yun-feng; ZHANG Hai-ying

doi:10.13873/J.1000-9787(2018)04-0020-04

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

更新时间：2025-09-11

- ECG beat classification method based on match template generated by improved K-means clustering
- Issue 4, Pages: 20-23(2018)
- 作者机构：
  
  1. 中国科学院大学微电子学院
  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
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2018)04-0020-04
  CLC： TP311.13
- Published：2018
- 稿件说明：
移动端阅览
CHEN Yong-bo, XU Jing-bo, WANG Yun-feng, et al. ECG beat classification method based on match template generated by improved K-means clustering[J]. 2018, (4): 20-23.
DOI：

CHEN Yong-bo, XU Jing-bo, WANG Yun-feng, et al. ECG beat classification method based on match template generated by improved K-means clustering[J]. 2018, (4): 20-23. DOI： 10.13873/J.1000-9787(2018)04-0020-04.

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。

Abstract

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.

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