华东师范大学学报(自然科学版) ›› 2005, Vol. 2005 ›› Issue (2): 27-32.

• 计算机科学 • 上一篇    下一篇

基于小波变换12-导联心电图特征提取方法

许建强1，袁震东2，李高平3，林靖宇3

  

  1. 1.华东师范大学计算机科学技术系,上海200062；2.华东师范大学数学系,上海200062；3.上海中山医院,上海200032
  • 收稿日期:2003-06-27 修回日期:2003-10-08 出版日期:2005-05-25 发布日期:2005-05-25
  • 通讯作者: 许建强

Feature Extraction Method for 12-Lead ECG Based on Wavelet Transform(Chinese)

XU Jian-qiang1,YUAN Zhen-dong2,LI Gao-ping3,LIN Jing-yu3   

  1. 1.Department of Computer Science and Technology,East China Normal University,Shanghai200062,China;2.Department of Mathematics,East China Normal University,Shanghai200062,China;3.Shanghai Zhongshan Hospital,Shanghai200032,China
  • Received:2003-06-27 Revised:2003-10-08 Online:2005-05-25 Published:2005-05-25
  • Contact: XU Jian-qiang

摘要：

提出了一种基于小波多分辨分析的算法对心电信号进行特征提取和识别.通过小波变换对常规12-导联心电图进行分段和特征提取, 并利用支撑向量机和提取的特征向量对未知心电图进行分类.实验结果表明该方法具有较好的应用前景.

关键词: 分段, 特征抽取, 小波变换, 支撑向量机, 心电图分类, 分段, 特征抽取, 小波变换, 支撑向量机, 心电图分类

Abstract: An algorithm to extract features of electro-cardio-gram (ECG) signal based on wavelet multi-resolution analysis is developed. The wavelet transform is used for the segmentation and the feature extraction of ordinary 12-lead ECG. The support vector machine is used to classify the unknown ECG signal. The result shows that feature extraction technology has an optimistic prospect.

Key words: feature extraction, wavelet transform, support vector machine, ECG classification, segmentation, feature extraction, wavelet transform, support vector machine, ECG classification

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