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. |
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. |
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. |
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. |
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. |
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. |
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. |
Wu Y, Zhang L. ECG classification using ICA features and support vector machines. Neural Information Processing, 2011, 7062: 146-154.
|
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. |
曹林林. 基于流形学习的分类技术. 济南: 山东师范大学, 2013.
|
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. |
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. |
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. |
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. |
Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290(550): 2319-2323.
|
16. |
Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(550): 2323-2326.
|
17. |
Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput, 2003, 15(6): 1373-1396.
|
18. |
Lashgari E, Jahed M, Khalaj B. Manifold learning for ECG arrhythmia recognition//Iranian Conference on Biomedical Engineering. Tehran, Iran, 2013: 126-131.
|
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. |
He X, Cai D, Yan S, et al. Neighborhood preserving embedding//Tenth IEEE International Conference on Computer Vision. Beijing, China, 2005: 1208-1213.
|
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. |
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. |
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. |
Cortes C, Vapnik V. Support-Vector networks. Mach Learn, 1995, 20(3): 273-297.
|
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. |
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. |
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. |
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. |
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.
|