Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

• Autorzy: Hoai Linh Tran , Van Nam Pham , Hoang Nam Vuong
• Języki publikacji: EN

Abstrakty

EN

The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston's Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers' performances and with other integration methods to show the high quality of the proposed solution.

Słowa kluczowe

EN

neural classifiers; integration of classifiers; decision tree; arrhythmia recognition; Hermite basis function decomposition

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: 24
• Numer: 3
• Strony: 647-655

Daty

wydano

2014

otrzymano

2013-05-24

poprawiono

2013-10-28

poprawiono

2013-12-16

Twórcy

autor

Hoai Linh Tran

• School of Electrical Engineering, Hanoi University of Science and Technology, Dai Co Viet Str., No. 1, Hanoi, Vietnam

autor

Van Nam Pham

• School of Electrical Engineering, Hanoi University of Science and Technology, Dai Co Viet Str., No. 1, Hanoi, Vietnam

autor

Hoang Nam Vuong

• School of Electrical Engineering, Hanoi University of Science and Technology, Dai Co Viet Str., No. 1, Hanoi, Vietnam

Bibliografia

• Can Ye, Vijaya Kumar, B.V.K. and Coimbra, M.T. (2012). Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification, Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), Arlington, VA, USA, pp. 2428-2431.
• de Chazal, P., O'Dwyer, M. and Reilly, R.B. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features, IEEE Transactions on Biomedical Engineering 51(7): 1196-1206.
• Chi-Hwa, S., Jun, W., Dong-Hun, S. and Won-Don, L. (2008). Solving multi-sensor problem with a new approach, Proceedings of the First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), Ostrava, Czech Republic, pp. 348-353.
• Haykin, S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Edition, Prentice-Hall, Englewood Cliffs, NJ.
• Hu, Y.H., Palreddy, S. and Tompkins, W. (1997). A patient adaptable ECG beat classifier using a mixture of experts approach, IEEE Transactions on Biomedical Engineering 44(9): 891-900.
• Huan, R. and Pan, Y. (2011). Decision fusion strategies for SAR image target recognition, Radar, Sonar & Navigation, IET 5(7): 747-755.
• Huifang, H., Guangshu, H. and Li, Z. (2010). Ensemble of support vector machines for heartbeat classification, Proceedings of the 10th IEEE International Conference on Signal Processing (ICSP), Beijing, China, pp. 1327-1330.
• Hsu, C.W. and Lin, C.J. (2002). A comparison methods for multi class support vector machines, IEEE Transactions on Neural Networks 13(2): 415-425.
• Jang, L., Sun, C.T. and Mizutani, E. (1997). Neuro-fuzzy and Soft Computing, Prentice-Hall, Englewood Cliffs, NJ.
• Kuncheva, L. (2004). Combining Pattern Classifiers: Methods and Algorithms, Wiley, Hoboken, NJ.
• Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L. and Sornmo, L. (1997). Clustering ECG complexes using Hermite functions and self-organizing maps, IEEE Transactions on Biomedical Engineering 47(7): 838-847.
• Łęski, J. (2003). A fuzzy if-then rule-based nonlinear classifier, International Journal of Applied Mathematics and Computer Science 13(2): 215-223.
• Moody, G. and Mark, R. (2001). The impact of the MIT-BIH Arrhythmia Database, IEEE Engineering in Medicine and Biology 20(3): 45-50.
• Melgani, F. and Bazi, Y. (2008). Classification of electrocardiogram signals with support vector machines and particle swarm optimization, IEEE Transactions on Information Technology in Biomedicine 12(5): 667-677.
• Monson, L. (1997). Algorithm Alley Column: C4.5, Dr. Dobbs, http://www.drdobbs.com.
• Nikias, C. and Petropulu, A. (1993). Higher-Order Spectra Analysis: A Nonlinear Signal Processing Framework, Prentice-Hall, Englewood Cliffs, NJ.
• Osowski, S. and Tran, H.L. (2001). ECG beat recognition using fuzzy hybrid neural network, IEEE Transactions on Biomedical Engineering 48(11): 1265-1271.
• Osowski, S. and Tran, H.L. (2003). On-line heart beat recognition using Hermite polynomials and neuro-fuzzy network, IEEE Transactions on Instrumentation and Measurement 52(4): 1224-1230.
• Osowski, S., Tran, H.L. and Markiewicz, T. (2004). Support vector machine based expert system for reliable heart beat recognition, IEEE Transactions on Biomedical Engineering 51(4): 582-589.
• Osowski, S., Markiewicz, T. and Tran, H.L. (2006). Ensemble of neural networks for improved recognition and classification of arrhythmia, Proceedings of the XVIII International Measurement Confederation World Congress, Rio de Janeiro, Brazil, pp. 201-206.
• Pagano, C., Granger, E., Sabourin, R. and Gorodnichy, D.O. (2012). Detector ensembles for face recognition in video surveillance, Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, pp. 1-8.
• Quinlan, J.R (1993). C4.5 Programs for Machine Learning, Morgan Kaufmann Publishers, San Francisco, CA.
• Ramirez, E., Castillo, O. and Soria, J. (2010). Hybrid system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and multi layer perceptrons combined by a fuzzy inference system, Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, pp. 1-6.
• Sajedin, A., Ebrahimpour, R. and Garousi, T.Y. (2011). Electrocardiogram beat classification using classifier fusion based on decision templates, Proceedings of the 5th IEEE International Conference on Cybernetic Intelligent Systems (CIS), Quindao, China, pp. 7-12.
• Scholkopf, B. and Smola, A. (2002). Learning with Kernels, MIT Press, Cambridge, MA.
• Troć, M. and Unold, O. (2010). Self-adaptation of parameters in a learning classifier system ensemble machine, International Journal of Applied Mathematics and Computer Science 20(1): 157-174, DOI: 10.2478/v10006-010-0012-8.
• Vapnik, V. (1998). Statistical Learning Theory, Wiley, New York, NY.
• Vapnik, V. (1999). The Nature of Statistical Learning Theory, Springer, New York.
• Woźniak, M. and Krawczyk, B. (2012). Combined classifier based on feature space partitioning, International Journal of Applied Mathematics and Computer Science 22(4): 855-866, DOI: 10.2478/v10006-012-0063-0.
• Ying, Y., Xiao-Long, W. and Bing-Quan, L. (2004). A gradual combining method for multi-SVM classifiers based on distance estimation, Proceedings of the 3rd International Conference on Machine Learning and Cybernetics, Shanghai, China, pp. 3434-3438.
• Yujin, Z., Yihua, T., Haitao L. and Haiyan, G. (2011). A multi-classifier combined decision tree hierarchical classification method, Proceedings of the 2011 International Symposium on Image and Data Fusion (ISIDF), Yunnan, China, pp. 1-3.
• Zellmer, E., Fei, S. and Hao, Z. (2009). Highly accurate ECG beat classification based on continuous wavelet transformation and multiple support vector machine classifiers, Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics, Tianjin, China, pp. 1-5.

Identyfikatory

DOI

10.2478/amcs-2014-0047