Electrocardiogram beat classification based on kernel principal component analysis and support vector machine

LIU Tong; SI Yu-juan; ZANG Mu-jun; WANG Di

doi:10.3788/OPE.20152313.0745

您当前的位置：

首页 >

文章列表页 >

Electrocardiogram beat classification based on kernel principal component analysis and support vector machine

更新时间：2020-08-13

- Electrocardiogram beat classification based on kernel principal component analysis and support vector machine
- Optics and Precision Engineering Vol. 23, Issue 10z, Pages: 744-751(2015)
- 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：
- DOI：10.3788/OPE.20152313.0745
  CLC： R54;TP391.4
- Received：02 June 2015，
  
  Revised：30 June 2015，
  
  Published：14 November 2015
- 稿件说明：
移动端阅览
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

references

高印寒,唐荣江,梁杰,等. 汽车声品质的GA-BP神经网络预测与权重分析[J]. 光学精密工程,2013,21(2):462-468. GAO Y H, TANG R J, LIANG J, et al.. Sound quality prediction and weight analysis of vehicles based on GA-BP neural network[J]. Opt. Precision Eng., 2013, 21(2):462-468.(in Chinese)

王灿进,孙涛,石宁宁,等. 基于双隐含层BP算法的激光主动成像识别系统[J]. 光学精密工程,2014,22(6):1639-1647. WANG C J, SUN T, SHI N N, et al.. Laser active imaging and recognition system based on double hidden layer BP algorithm[J]. Opt. Precision Eng., 2014, 22(6):1639-1647.(in Chinese)

THOMAS M, DAS M K, ARI S. Automatic ECG arrhythmia classification using dual tree complex wavelet based features[J]. AEU-International Journal of Electronics and Communications, 2015, 69(4):715-721.

ÖZBAY Y, TEZEL G. A new method for classification of ECG arrhythmias using neural network with adaptive activation function[J]. Digital Signal Processing, 2010, 20(4):1040-1049.

PORÉE F, KACHENOURA A, CARRAULT G, et al.. Surface electrocardiogram reconstruction from intracardiac electrograms using a dynamic time delay artificial neural network[J]. IEEE Transactions on Biomédical Engineering, 2013, 60(1):106-114.

MELIN P, AMEZCUA J, VALDEZ F, et al.. A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias[J]. Information Sciences, 2014, 279:483-497.

IMAH E M, JATMIKO W, BASARUDDIN T. Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization(AMGLVQ):Integrating Feature Extraction and Classification[J]. International Journal on Smart Sensing and Intelligent Systems, 2013, 6:1891-1917.

IMAH E M, JATMIKO W, BASARUDDIN T. Adaptive Multilayer Generalized Learning Vector Quantization(AMGLVQ) as new algorithm with integrating feature extraction and classification for Arrhythmia heartbeats classification[C]. 2012 IEEE International Conference on Systems, Man, and Cybernetics(SMC), 2012:150-155.

RACHMADI M F, MA'SUM M A, JATMIKO W. Fuzzy learning vector quantization particle swarm optimization(FLVQ-PSO) and fuzzy neuro generalized learning vector quantization(FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram[C]. 2012 Proceedings of IEEE SICE Annual Conference(SICE), 2012:465-470.

DUTTA S, CHATTERJEE A, MUNSHI S. Identification of ECG beats from cross-spectrum information aided learning vector quantization[J]. Measurement, 2011, 44(10):2020-2027.

TAWAKAL M I, SURYANA M E, NOVIYANTO A, et al.. Analysis of multi codebook GLVQ versus standard GLVQ in discriminating sleep stages[C]. 2012 International Conference on Advanced Computer Science and Information Systems(ICACSIS), 2012:197-202.

PAUWS S C, BIEHL M. Insightful stress detection from physiology modalities using Learning Vector Quantization[J]. Neurocomputing, 2015, 151:873-882.

朱福珍,李金宗,朱兵,等. 基于径向基函数神经网络的超分辨率图像重建[J]. 光学精密工程,2010,18(6):1444-1451. ZHU F ZH, L J Z, ZHU B, et al.. Super-resolution image reconstruction based on RBF neural network[J]. Opt. Precision Eng., 2010, 18(6):1444-1451.(in Chinese)

KORVREK M, DOAN B. ECG beat classification using particle swarm optimization and radial basis function neural network[J]. Expert systems with Applications, 2010, 37(12):7563-7569.

MELGANI F, BAZI Y. Classification of electrocardiogram signals with support vector machines and particle swarm optimization[J]. IEEE Transactions on Information Technology in Biomedicine, 2008, 12(5):667-677.

ACHARYA U R, VIDYA K S, GHISTA D N, et al.. Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method[J]. Knowledge-Based Systems, 2015, 81:56-64.

MPORAS I, TSIRKA V, ZACHARAKI E I, et al.. Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients[J]. Expert Systems with Applications, 2015, 42(6):3227-3233.

YE C, COIMBRA M T, KUMAR B. Arrhythmia detection and classification using morphological and dynamic features of ECG signals[C]. 2010 Annual International Conference of the IEEE. Engineering in Medicine and Biology Society(EMBC), 2010:1918-1921.

DUTTA S, CHATTERJEE A, MUNSHI S. Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification[J]. Medical Engineering & Physics, 2010, 32(10):1161-1169.

MOAVENIAN M, KHORRAMI H. A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification[J]. Expert Systems with Applications, 2010, 37(4):3088-3093.

KIM H, YAZICIOGLU R F, MERKEN P, et al.. ECG signal compression and classification algorithm with quad level vector for ECG holter system[J]. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(1):93-100.

BAYDOGAN M G, RUNGER G, TUV E. A bag-of-features framework to classify time series[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11):2796-2802.

WANG J, LIU P, SHE M F H, et al.. Bag-of-words representation for biomedical time series classification[J]. Biomedical Signal Processing and Control, 2013, 8(6):634-644.

WANG J, LIU P, SHE M F H, et al.. Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model[J]. Computer Methods and Programs in Biomedicine, 2013, 111(3):629-641.

WANG J, SUN X, SHE M F H, et al.. Unsupervised mining of long time series based on latent topic model[J]. Neurocomputing, 2013, 103:93-103.

LIU T, SI Y, WEN D, et al.. Vector quantization for ECG beats classification[C].2014 IEEE 17th International Conference on Computational Science and Engineering(CSE), 2014:13-20.

Views

813

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Microscopic feature localization for mass precision assembly tasks

Crack detection and segmentation in CT images using Hessian matrix and support vector machine

Classification of real-time digital PCR amplification curves

Threat assessment of support vector machine optimized by Krill Herd algorithm

Related Author

CUI Shipeng

LU Shiqin

XU Zheng

YU Zhongyang

WANG Xiaodong

Zhongyang YU

Xiaodong WANG

Zheng XU

Related Institution

School of Mechanical Engineering， Dalian University of Technology

ICT Research Center， Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China， Chongqing University

College of Optoelectronic Engineering， Chongqing University

University of Science and Technology of China

Ji Hua Laboratory

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰