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A Trainable Neural Network Ensemble for ECG Beat Classification

Authors: Atena Sajedin, Shokoufeh Zakernejad, Soheil Faridi, Mehrdad Javadi, Reza Ebrahimpour

Abstract:

This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptrons (MLPs) neural networks with different topologies are designed. The performance of the different combination methods as well as the efficiency of the whole system is presented. Among them, Stacked Generalization as a proposed trainable combined neural network model possesses the highest recognition rate of around 95%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems. ECG samples attributing to the different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study.

Keywords: ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329306

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References:


[1] R. Acharya, P.S. Bhat, S.S. Iyengar, A. Roo and S. Dua, Classification of heart rate data using artificial neural network and fuzzy equivalence relation, Pattern Recognition, vol. 36, no. 1, pp. 61-68, 2003.
[2] S. Osowski and T.H. Linh, ECG beat recognition using fuzzy hybrid neural network, IEEE Trans. Biomed. Eng., vol. 48, no. 11, pp. 1265- 1271, 2001.
[3] O. Pichler, A. Teuner and B.J. Hosticka, A comparison of texture feature extraction using adaptive Gabor filtering, pyramidal and tree structured wavelet transforms, Pattern Recognition, vol. 29, no. 5, pp. 733-742, 1996.
[4] Z. Dokur, T. Ölmez and E. Yazgan, Comparison of discrete wavelet and Fourier transforms for ECG beat classification, Electron. Lett., vol. 35, no. 18, 1999.
[5] K. Sternickel, Automatic pattern recognition in ECG timeseries, Comput. Meth. Prog. Biomed. 68 109-115,2002.
[6] Z. Dokur and T. Olmez, ECG beat classification by a novel hybrid neural network, Comput. Meth. Prog. Biomed., vol. 66, pp. 167-181, 2001.
[7] X. Wang and K. Paliwal, Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition, Pattern Recognition, vol. 36, pp. 2429-2439, 2003.
[8] Y. Ozbay, R. Ceylan and B. Karlik, A fuzzy clustering neural network architecture for classification of ECG arrhythmias, Computers in Biology and Medicine, vol. 36, pp. 376-388, 2006.
[9] A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis, IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626-634, 1999.
[10] Z. Wang, Z. He and J. Z. Chen, Blind EGG separation using ICA neural networks, In Proceedings of the 19th annual international conference of the IEEE-EMBS, Chicago, IL. USA, vol. 3, pp. 1351-1354, 1997.
[11] Y. Ozbay and B. Karlik, A recognition of ECG arrhythmias using artificial neural network, Proceedings of the 23rd Annual Conference, IEEE/EMBS, Istanbul, Turkey, 2001.
[12] Y. Ozbay, Fast recognition of ECG arrhythmias, Ph.D. Thesis, Institute of Natural and Applied Science, Selcuk University, 1999.
[13] S.Y. Foo, G. Stuar and B. Harvey, A. Meyer-Baese, Neural networkbased ECG pattern recognition, Eng. Appl. Artif. Intell., vol. 15, pp. 253-260, 2002.
[14] R. Ebrahimpour, E. Kabir, H. Esteky and M. R. Yousefi, A mixture of multilayer perceptron experts network for modeling face/nonface recognition in cortical face processing regions, Intelligent Automation and Soft Computing, vol. 14, no. 2, pp. 145-156, 2008.
[15] R. Ebrahimpour, E.Kabir, H. Esteky, M. R. Yousefi, View-independent face recognition with mixture of experts, Neurocomputing, vol. 71, no. 4-6, pp. 1103-1107, 2008.
[16] L.Kuncheva, Combining pattern classifiers: Methods and algorithms. New York: Wiley, 2004.
[17] V. Pilla and H.S. Lopes, Evolutionary training of a neuro-fuzzy network for detection of P wave of the ECG, Proceedings of the Third International Conference on Computational Intelligence and Multimedia Applications, New Delhi, India, pp. 102-106, 1999.
[18] M. Engin and S. Demira╦ÿg, Fuzzy-hybrid neural network based ECG beat recognition using three different types of feature set, Cardiovasc. Eng. Int. J., vol. 3, no. 2, pp. 71-80, 2003.
[19] I. Guler, and E. D. Ubeyli, A mixture of experts network structure for modelling Doppler ultrasound blood flow signals, Computers in Biology and Medicine, vol. 35, no. 7, pp. 565-582, 2005.
[20] I. Guler, and E. D. Ubeyli, ECG beat classifier designed by combined neural network model, Pattern Recognition, vol. 38, no. 2, pp. 199-208, 2005.
[21] E.D. Ubeyli, Support vector machines for detection of electrocardiographic changes in partial epileptic patients, Engineering Applications of Artificial Intelligence, vol. 21, pp. 1196-1203, 2008.
[22] D.H. Wolpert, Stacked generalization, Neural Networks, vol. 5, pp. 241- 259, 1992.
[23] D. Donoho and I. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200-1223, 1995.
[24] D.Donoho, De-noising by soft-thresholding, in: IEEE Transactions on Information Theory, vol. 41, pp. 613-627, 1995.
[25] Daubechies, I., Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.
[26] Amara Grap, An Introduction to Wavelets, IEEE Comp.Sc. And Eng., Vol. 2, No. 2, 1995.
[27] M.A. Al-Alaoui, A unified analog and digital design to peak and valley detector window peak and valley detectors and zero crossing detectors, IEEE Trans. Instrum. Meas., vol. 35, pp. 304-307, 1986.
[28] S. Haykin, Neural networks: A comprehensive foundation, USA: Prentice Hall, 1999.
[29] K. Woods, W.P. Kegelmeyer and K. Bowyer, Combination of multiple classifiers using local accuracy estimates, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 405-410, 1997.
[30] L. A. Rastrigin and R. H. Erenstein, Method of collective recognition. Moscow: Energoizdat (in Russian), 1982.
[31] R. A. Jacobs, M. I. Jordan, S. E. Nowlan and G. E. Hinton, Adaptive mixture of experts, Neural Computing, vol. 3, pp. 79-87, 1991.
[32] E. Alpaydin and M. I. Jordan, Local linear perceptrons for classification, IEEE Transactions on Neural Networks, vol. 7, no. 3, pp. 788-792, 1996.
[33] L. Xu, A. Krzyzak and C. Suen, Methods of combining multiple classifiers and their application to handwriting recognition, IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, pp. 418-435, 1992.
[34] K. C. Ng, and B. Abramson, Consensus diagnosis: A simulation study, IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, pp. 916- 928, 1992.
[35] R. Pektatli,Y. Ozbay,M. Ceylan and B. Karlik, Classification of ECG signals using fuzzy clustering neural networks (FCNN), Proceedings of the International XII, TAINN-03, vol.1, no. 1, Canakkale, Turkey, pp. 105-108, 2003.
[36] H. Nikoo, M. Azarpeikan, M. R. Yousefi, R. Ebrahimpour and A. Shahrabadi, Using a trainable neural network ensemble for trend prediction of Tehran stock exchange, International Journal of Computer Science and Network Security, vol. 12, pp. 287-293, 2007.
[37] R.G. Mark and G.B. Moody, MIT-BIH Arrhythmia Database 1997. Available from: .
[38] G.B. Moody and R.G. Mark, The impact of the MIT/BIH arrhythmia database, IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, pp. 45-50, 2001.