A Trainable Neural Network Ensemble for ECG Beat Classification
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.
[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: <http://www.ecg.mit.edu/dbinfo.html>.
[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.
[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: <http://www.ecg.mit.edu/dbinfo.html>.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:50061", author = "Atena Sajedin and Shokoufeh Zakernejad and Soheil Faridi and Mehrdad Javadi and Reza Ebrahimpour", title = "A Trainable Neural Network Ensemble for ECG Beat Classification", 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", volume = "4", number = "9", pages = "394-7", }
