计算机应用 ›› 2013, Vol. 33 ›› Issue (04): 1176-1178.DOI: 10.3724/SP.J.1087.2013.01176

• 典型应用 • 上一篇    下一篇

基于小波包分析的经络心电信号熵特征提取

刘鑫,何宏,谭永红   

  1. 上海师范大学 信息与机电工程学院,上海 200234
  • 收稿日期:2012-10-24 修回日期:2012-11-27 出版日期:2013-04-01 发布日期:2013-04-23
  • 通讯作者: 何宏
  • 作者简介:刘鑫(1989-),男,江西吉安人,硕士研究生,主要研究方向:模式识别、医学信息处理;何宏(1973-),女,四川射洪人,副教授,博士,主要研究方向:模式识别、智能信息处理;谭永红(1958-),男,广西桂林人,教授,博士,主要研究方向:系统建模、智能控制、生物医学信号处理。
  • 基金资助:

    国家自然科学基金资助项目(60971004,61171088;上海市教委科研创新项目(13YZ056)

Feature extraction of energy entropy of ECG signal on meridian systems using wavelet packet analysis

LIU Xin,HE Hong,TAN Yonghong   

  1. College of Information, Mechanical and Electronic Engineering, Shanghai Normal University, Shanghai 200234, China
  • Received:2012-10-24 Revised:2012-11-27 Online:2013-04-23 Published:2013-04-01
  • Contact: HE Hong

摘要: 为研究人体经络特征,提出了基于小波包分析的经络穴位心电信号熵特征提取的方法。首先通过建立经络检测实验采集了经络测试点的心电信号,然后采用小波包对经络心电信号进行三层分解,并根据重构后的心电信号提取经络穴位的熵特征。同时采用了K-means和模糊C均值聚类方法实现了穴位点和非穴位点的有效分类。研究结果表明经络上穴位点心电信号的能量熵明显大于非穴位点的熵值,并且这一特征可以作为区分经络穴位点和非穴位点的有力科学依据。

关键词: 经络穴位, 心电信号, 小波包, 熵, 特征提取, 聚类

Abstract: In order to study meridian characteristics, a feature extraction method of ElectroCardioGraph (ECG) signal on the meridian based on wavelet packet analysis and energy entropy was proposed. A meridian measuring experiment was firstly built to complete the acquisition of meridian data. Then meridian ECG signals were decomposed by a three layer wavelet packet decomposition. Energy entropy features of meridian ECG signals were extracted according to the results of signal reconstruction. After that, both K-means and Fuzzy C-Means (FCM) clustering techniques realized the effective partition of acupoints and non-acupoints. The derived clustering results indicate that the energy entropy values of ECG signals on the acupoints are obviously higher than those on the non-meridian points. It can be used as a powerful scientific basis for the discrimination of acupoints and non-acupoints.

Key words: meridian acupoint, ElectroCardioGraph (ECG) signal, wavelet packet, entropy, feature extraction, clustering