Heart beat classification based on feature fusion by principle component analysis

WANG Di; SI Yu-juan; LIU Tong; GUO Yong-cong; LIU Shu-wen

doi:10.3788/OPE.20152313.0453

您当前的位置：

首页 >

文章列表页 >

Heart beat classification based on feature fusion by principle component analysis

更新时间：2020-08-13

- Heart beat classification based on feature fusion by principle component analysis
- Optics and Precision Engineering Vol. 23, Issue 10z, Pages: 453-458(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. 北京大学软件与微电子学院北京,100871
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152313.0453
  CLC： R540.4+1;TP391.4
- Received：15 April 2015，
  
  Revised：07 May 2015，
  
  Published：14 November 2015
- 稿件说明：
移动端阅览
王迪, 司玉娟, 刘通等. 基于主成分分析进行特征融合的心拍分类[J]. 光学精密工程, 2015,23(10z): 453-458

WANG Di, SI Yu-juan, LIU Tong etc. Heart beat classification based on feature fusion by principle component analysis[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 453-458
王迪, 司玉娟, 刘通等. 基于主成分分析进行特征融合的心拍分类[J]. 光学精密工程, 2015,23(10z): 453-458 DOI： 10.3788/OPE.20152313.0453.

WANG Di, SI Yu-juan, LIU Tong etc. Heart beat classification based on feature fusion by principle component analysis[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 453-458 DOI： 10.3788/OPE.20152313.0453.

摘要

针对心拍分类中单一特征分类准确率低而组合特征存在特征冗余和维度过高的问题

提出了一种基于主成分分析(PCA)进行特征融合的心拍分类算法。该算法在对单一特征进行规范化后

将多种特征组合为一个具有大量信息的高维特征;之后引入PCA算法去除冗余成分

得到低维融合特征;最后

利用支持向量机(SVM)为分类器完成心拍分类。以心拍时域特征、离散小波特征、离散傅里叶特征为例

在MIT-BIH数据库上进行了实验。实验结果表明

融合特征维度为100时

分类准确率可达97.389%。与单一特征相比

融合特征提高了分类准确率;与组合特征相比

融合特征降低了特征维度。

Abstract

For lower accuracy by a single feature and too high dimensionality by a combined feature in heartbeat classification

a new heartbeat classification algorithm was proposed based on the feature fusion by using Principle Component Analysis(PCA). With proposed algorithm

each single feature was normalized

then all kinds of features were combined together to a new one with high dimensionality and more information. In order to reduce dimensionality of combined features

the PCA was employed to remove redundant components. Finally

the Support Vector Machine(SVM) was used as a classifier to classify different heartbeats. By taking time domain feature

Discrete Wavelet Trans form(DWT) feature and Discrete Fourier Transform(DFT) feature as examples

the experiments were performed in MIT-BIH database. This study was compared with the new feature mentioned above

three single features and combined feature without Principle Component Analysis. The experiments results indicate that the accuracy of the new features is 97.389% when its dimension is 100. The new fusion feature has lower dimension than combined feature and higher classification accuracy than single feature.

关键词

Keywords

references

PHILIP C,MARIA O'D,RICHARD B. Automatic classification of heartbeart using ECG morphology and heartbeat interval features[J].IEEE Transactions on Biomedical Engineering,2004(51):1196-1206.

HARI M R,ANURAG T,SHAILJA S. ECG signal processing for abnormalities detection using multi-resolution wavelet transform and artificial neural network classifier[J].Measurement,2013(46):3238-3246.

林海明,杜子芳. 主成分分析综合评价应该注意的问题[J]. 统计研究, 2013,30(8):25-31. LIN H M,DU Z F.Some problems in comprehensive evaluation in the principal component analysis[J].Statistical Research,2013,30(8):25-31.(in Chinese)

唐红,郑文斌,李宪霞. 主成分分析在光全散射特征波长选择中的应用[J]. 光学精密工程,2010,18(8):1691-1698. TANG H,ZHENG W B,LI X X.Application of principal component analysis to selection of characteristic wavelengths with total light scattering[J].Opt. Precision Eng.,2010,18(8):1691-1698.(in Chinese).

王丽荣,王建蕾. 基于主成分分析的唇部轮廓建模[J]. 光学精密工程,2012,20(12):2768-2772. WANG L R, WANG J L.Lip contour modeling based on PCA[J].Opt. Precision Eng.,2012,20(12):2768-2772.(in Chinese)

付小宁,张涛,万里. 基于多光谱分离的烟雾检测[J]. 光学精密工程,2013,21(11):2798-2802. FU X N,ZHANG T,WAN L.Smoke detection based on multispectral separation[J].Opt. Precision Eng.,2013,21(11):2798-2802.(in Chinese)

范增飞. 基于频域特征的心电图分类研究[D].天津:天津大学,2006. FAN Z F.ECG classification based on frequency domain features[D].Tianjin:Tianjin University,2006.(in Chinese)

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

YU S N, CHEN Y H. Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network[J].Pattern Recognition Letters,2007,28(10):1142-1150.

KHORRAMI H, MOAVENIAN M. A comparative study of dwt, cwt and dct transformations in ecg arrhythmias classification[J].Expert Systerms with Applications,2010,37(8):5751-5757.

蔡哲元,余建国,李先鹏,等.基于核空间距离测度的特征选择[J]. 模式识别与人工智能,2010,23(2):235-240. CAI Z Y,YU J G,LI X P,et al..Feature selection algorithm based on kernel distance measure[J].Pattern Recognition and Artificial Intelligence,2010,23(2):235-240.(in Chinese)

韩景梅. 支持向量机决策树算法研究及其应用[D].上海:上海交通大学,2007. HAN J M. Research on support vector machine-decision tree arithmetic and its application[D].Shanghai:Shanghai Jiao Tong University,2007.(in Chinese)

WANG D K,LI D W, LIN Y. A new method of face recognition with data field and PCA[C].2013 IEEE International Conference on Granular Computing(Grc),Beijing, 2013:320-325.

ZHAI M H, SHI F Y,Duncan D.Convariance-Based PCA for multi-size data[C].2014 22nd International Conference on Pattern Recognition, Stockholm, 2014:1603-1608.

LIM S T,YAP D F W,MANAP N A. Medical image compression using block-based PCA algorithm[C].2014 International Conference on Computer, Communications, and Control Technology, Langkawei, Kedah, 2014:171-175.

Views

369

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Enhanced edge feature extraction dual branch fusion network for real image dehazing

Lightweight video super-resolution based on hybrid spatio-temporal convolution

Joint self-attention and branch sampling for object detection on drone imagery

Multidimensional attention mechanism and selective feature fusion for image super-resolution reconstruction

Cross-scale infrared pedestrian detection based on dynamic feature optimization mechanism

Related Author

XIE Tao

WANG Hongliang

ZHANG Kaomin

XIA Fengling

LI Xiongxin

CHEN Hao

XIA Zhenping

ZHANG Yuning

Related Institution

Faculty of Civil Aviation and Aeronautics，Kunming University of Science and Technology

Cheng Laboratory for Information Display and Visualization

Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center

Display R&D Centre， School of Electronic Science & Engineering， Southeast University

School of Electronic & Information Engineering， Suzhou University of Science and Technology

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