基于核主元分析和支持向量机的心拍识别

刘通; 司玉娟; 臧睦君; 王迪

doi:10.3788/OPE.20152313.0745

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基于核主元分析和支持向量机的心拍识别

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- 基于核主元分析和支持向量机的心拍识别
- Electrocardiogram beat classification based on kernel principal component analysis and support vector machine
- 光学精密工程 2015年23卷第10z期页码：744-751
- 作者机构：
  
  1. 吉林大学通信工程学院,吉林长春,中国,130012
  2. 吉林大学珠海学院,广东珠海,519041
  3. 鲁东大学信息与电气工程学院, 山东烟台 264025
- 作者简介：
- 基金信息：
  
  吉林省重点科技攻关项目(No.20150204039GX)();吉林省长春市重大科技攻关专项资助项目(No.14KG064)();广东省科技计划资助项目(No.2013
- DOI：10.3788/OPE.20152313.0745
  中图分类号： R54;TP391.4
- 收稿日期：2015-06-02，
  
  修回日期：2015-06-30，
  
  纸质出版日期：2015-11-14
- 稿件说明：
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刘通, 司玉娟, 臧睦君等. 基于核主元分析和支持向量机的心拍识别[J]. 光学精密工程, 2015,23(10z): 744-751

LIU Tong, SI Yu-juan, ZANG Mu-jun etc. Electrocardiogram beat classification based on kernel principal component analysis and support vector machine[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 744-751
刘通, 司玉娟, 臧睦君等. 基于核主元分析和支持向量机的心拍识别[J]. 光学精密工程, 2015,23(10z): 744-751 DOI： 10.3788/OPE.20152313.0745.

LIU Tong, SI Yu-juan, ZANG Mu-jun etc. Electrocardiogram beat classification based on kernel principal component analysis and support vector machine[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 744-751 DOI： 10.3788/OPE.20152313.0745.

摘要

为了提高心拍识别的准确率

对心拍识别的分类算法进行了研究

提出基于核主元分析和支持向量机(KPCA-SVM)的心拍分类算法。该算法采用核函数对心拍的特征进行高维变换形成核矩阵;在高维空间下对心拍核矩阵进行主元分析

实现降维与去噪。最后

使用线性支持向量机分类器对降维和去噪后的核矩阵进行分类。为了评估提出算法的有效性

在MIT-BIH-AHA数据集上与核支持向量机及BP(Back Propagation)、径向基函数(RBF)、学习矢量量化(LÜQ)等神经网络方法展开对比。实验结果表明:核主元分析可以将核支持向量机的分类准确率提高1.16%

达到了95.98%

且识别准确率高于神经网络方法。得到的结果验证了提出的方法可以有效提高心拍识别的准确率。

Abstract

To increase the electrocardiogram(ECG) classification accuracy

this study focuses on a method of ECG beat classification and proposes a KPCA-SVM(kernel principal component analysis-support vector machine) classification algorithm. Firstly

the kernel function was used to perform high dimensional transform for the ECG beats to form a kernel matrix. Then

the ECG beat kernel matrix was perform the principal component analysis under a high dimensional space to implement the dimension reduction and denoising of kernel matrix. Finally

a linear SVM classifier was employed to classify beats according to the dimension reduced kernel matrix. In order to evaluate the effectiveness of the algorithm proposed

it was applied in MIT-BIH-AHA dataset for ECG beat classification

and then compared with KSVM(Kernel Support Vector Machine) and artificial neural network such as Back Propagation(BP)

Radical Basis Function(RBF)

and Learning Vector Quantization(LVQ). The results show that the proposed algorithm improves the classification accuracy of KSVM by 1.16%

reaching 95.98%

which is much higher than that of the artificial neural networks mentioned above. Consequently

it is verified that the algorithm proposed can effectively improve the classification accuracy.

关键词

Keywords

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