计算机工程 ›› 2012, Vol. 38 ›› Issue (9): 202-204,207.doi: 10.3969/j.issn.1000-3428.2012.09.061

• 人工智能及识别技术 • 上一篇    下一篇

基于近似熵的心肌猝死预警诊断

汤丽平a,刘 剑b   

  1. (重庆医科大学附属第一医院 a. 设备处;b. 心内科,重庆 400016)
  • 收稿日期:2011-08-29 出版日期:2012-05-05 发布日期:2012-05-05
  • 作者简介:汤丽平(1986-),女,硕士,主研方向:模式识别; 刘 剑,副教授
  • 基金项目:
    重庆市卫生局基金资助项目[渝医(2006)27号文]

Warning Diagnosis of Sudden Cardiac Death Based on Approximate Entropy

TANG Li-ping   a, LIU Jian   b   

  1. (a. Department of Equipment; b. Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China)
  • Received:2011-08-29 Online:2012-05-05 Published:2012-05-05

摘要: 为提高传统线性分析方法的准确性,提出一种非线性动力学与时频分析相结合的心电信号分类方法。利用经验模式分解和阈值法抑制噪声,从而更有效地分解心电信号得到内蕴模式函数,分别计算其近似熵特征值,利用支持向量机分类器验证特征值的分类效果。实验结果表明,该方法能有效实现信号的自动分类识别,简便快速地初步诊断心肌猝死疾病的发生,正常和异常心电信号的分类识别准确率均达到90%以上。

关键词: 心电图, 心肌猝死, 非线性动力学, 经验模式分解, 近似熵, 支持向量机

Abstract: In order to improve the accuracy of traditional linear dynamics method, an automated Empirical Mode Decomposition(EMD) signals recognition method is proposed. The method which combines the nonlinear dynamics with the time-frequency analysis is used to study signals. In order to effectively restrain noise and decompose the signals, EMD is employed to preprocess the sample data, and the approximate entropy of some important intrinsic mode functions can be obtained. Support Vector Machine(SVM) is adopted to achieve the optimal classification. Experimental results show the correct classification rate can be more than 90%, which proves the reliability and veracity of the method.

Key words: Electrocardiogram(ECG), Sudden Cardiac Death(SCD), nonlinear dynamics, Empirical Mode Decomposition(EMD), approximate entropy, Support Vector Machine(SVM)

中图分类号: