全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2017 

基于近邻保持嵌入算法的心律失常心拍分类

DOI: doi:10.7507/1001-5515.201605045

Keywords: 心律失常, 近邻保持嵌入, 心电图, 支持向量机

Full-Text   Cite this paper   Add to My Lib

Abstract:

心律失常是一种极其常见的心电活动异常症状,基于心电图(ECG)的心拍分类对心律失常的临床诊断具有十分重要的意义。本文提出一种基于流形学习的特征提取方法——近邻保持嵌入(NPE)算法,实现心律失常心拍的自动分类。分类系统利用NPE算法获取高维心电节拍信号的低维流形结构特征,然后将特征向量输入支持向量机(SVM)分类器进行心拍的分类诊断。实验基于 MIT-BIH 心律失常数据库提供的 ECG 数据,对 14 种类型的心律失常心拍进行分类,总体分类准确率高达 98.51%。实验结果表明,所提方法是一种有效的心律失常心拍分类方法

References

[1]  1. 陆海燕, 陈翠杰, 娄丽艳, 等. 心律失常患者的临床观察与护理干预. 中国实用医药, 2013, 8(2): 209-210.
[2]  2. 武扬. 心电特征提取及分类方法研究. 上海: 上海交通大学, 2012.
[3]  3. Zadeh A E, Khazaee A, Ranaee V. Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput Methods Programs Biomed, 2010, 99(2): 179-194.
[4]  4. Korürek M, Nizam A. Clustering MIT-BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients. Digit Signal Process, 2010, 20(4): 1050-1060.
[5]  5. Lin C C, Yang C M. Heartbeat classification using normalized RR intervals and morphological features. Mathematical Problems in Engineering, 2014, 2014(12): 1-11.
[6]  6. de Chazal P, O'Dwyer M, Reilly R B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng, 2004, 51(7): 1196-1206.
[7]  7. Zhang H, Zhang L Q. ECG analysis based on PCA and Support Vector Machines//International Conference on Neural Networks and Brain. Beijing, China, 2005, 2: 743-747.
[8]  8. Wu Y, Zhang L. ECG classification using ICA features and support vector machines. Neural Information Processing, 2011, 7062: 146-154.
[9]  9. Ye Can, Kumar B V, Coimbra M T. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng, 2012, 59(10): 2930-2941.
[10]  10. 曹林林. 基于流形学习的分类技术. 济南: 山东师范大学, 2013.
[11]  11. Kallas M, Francis C, Kanaan L, et al. Multi-class SVM classification combined with kernel PCA feature extraction of ECG signals//Proceedings of International Conference on Telecommunications. Jounieh, Lebanon, 2012: 1-5.
[12]  12. 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//2015光学精密工程论坛论文集. 长春, 2105: 745-752.
[13]  13. 赵勇, 洪文学, 孙士博. 基于多特征和支持向量机的心律失常分类. 生物医学工程学杂志, 2011, 39(2): 292-295.
[14]  14. Li Hongqiang, Liang Huan, Miao Chunjiao, et al. Novel ECG signal classification based on KICA nonlinear feature extraction. Circuits Systems and Signal Processing, 2016, 35(4): 1187-1197.
[15]  15. Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290(550): 2319-2323.
[16]  16. Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(550): 2323-2326.
[17]  17. Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput, 2003, 15(6): 1373-1396.
[18]  18. Lashgari E, Jahed M, Khalaj B. Manifold learning for ECG arrhythmia recognition//Iranian Conference on Biomedical Engineering. Tehran, Iran, 2013: 126-131.
[19]  19. Vemulapati M. Classification of ECG arrhythmia using manifold learning and support vector machine[R/OL]. (2015-04-02)[2016-5-18]. http://www.usc.edu/CSSF/History/2015/Projects/35186.pdf.
[20]  20. He X, Cai D, Yan S, et al. Neighborhood preserving embedding//Tenth IEEE International Conference on Computer Vision. Beijing, China, 2005: 1208-1213.
[21]  21. Moody G B, Mark R G. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 2001, 20(3): 45-50.
[22]  22. Yazdani S, Vesin J M. Adaptive mathematical morphology for QRS fiducial points detection in the ECG//41st Computing in Cardiology Conference. Cambridge, MA, USA, 2014: 725-728.
[23]  23. Yeh Y C, Wang W J. QRS complexes detection for ECG signal: the Difference Operation Method. Comput Methods Programs Biomed, 2008, 91(3): 245-254.
[24]  24. Cortes C, Vapnik V. Support-Vector networks. Mach Learn, 1995, 20(3): 273-297.
[25]  25. 胡国胜, 钱玲, 张国红. 支持向量机的多分类算法. 系统工程与电子技术, 2006, 28(1): 127-132.
[26]  26. Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3, SI): 389-396.
[27]  27. übeyli E D. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Computer Methods & Programs in Biomedicine, 2009, 93(3): 313-321.
[28]  28. Karpagachelvi D S. Classification of ECG signals using particle swarm optimization and extreme learning machine. International Journal of Engineering Sciences & Research Technology, 2014, 3(7): 95-102.
[29]  29. Chen Y H, Yu S N. Selection of effective features for ECG beat recognition based on nonlinear correlations. Artif Intell Med, 2012, 54(1): 43-52.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133