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A novel approach in ECG beat recognition using adaptive neural fuzzy filter

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DOI: 10.4236/jbise.2009.22015    6,336 Downloads   15,963 Views   Citations

ABSTRACT

Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detec-tion has been the subject of considerable re-search efforts in recent years. Intelligent com-puting tools such as artificial neural network (ANN) and fuzzy logic approaches are demon-strated to be competent when applied individu-ally to a variety of problems. Recently, there has been a growing interest in combining both of these approaches, and as a result, adaptive neural fuzzy filters (ANFF) [1] have been evolved. This study presents a comparative study of the classification accuracy of ECG signals using (MLP) with back propagation training algorithm, and a new adaptive neural fuzzy filter architec-ture (ANFF) for early diagnosis of ECG ar-rhythmia. ANFF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules [1]. In this paper we used an adap-tive neural fuzzy filter as an ECG beat classifier. We combined 3 famous wavelet transforms and used them mid 4 the order AR model coefficient as features. Our results suggest that a new proposed classifier (ANFF) with these features can generalize better than ordinary MLP archi-tecture and also learn better and faster. The results of proposed method show high accu-racy in ECG beat classification (97.6%) with 100% specificity and high sensitivity.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Golpayegani, G. and Jafari, A. (2009) A novel approach in ECG beat recognition using adaptive neural fuzzy filter. Journal of Biomedical Science and Engineering, 2, 80-85. doi: 10.4236/jbise.2009.22015.

References

[1] C.T. Lin, C.F. Juang, (2001) “An adaptive neural fuzzy filter and its applications,” IEEE Transactions On Systems, MAN, And Cybernetics, VOL. 27, NO. 4, 1103-1110.
[2] S. Osowaki, T.H. Linh, (2001) “ECG beat recognition using fuzzy hybrid neural network,” IEEE Trans. Biomed. Eng. 48 (11) 1265-1271.
[3] Y. Ozbay, B. Karlik, (2001) “A reconition of ECG arrhythmias using artificial neyral network,” Proceedings of the 23rd Annual Conference, IEEE/EMBS, Istanbul, Turkey, pp. 76-80.
[4] Y. Ozbay, “Fast recognition of ECG arrhythmias,” (1999) Ph.D. Yhesis, Institute of Natural and Applied Science, Selcuk University
[5] S.Y. Foo, G. Harvey, A. Meyer-Baese, (2002) “Neural network- based ECG pattern recognition”, Eng. Appl. Artif. Intell. 15, 353-360.
[6] V. Pilla, H.S. Lopes, (1999) “Evolutionary training of a neuro-fuzzy network for detection of a P wave of the ECG,” Proceeding of the third international conference on computational intelligence and multimedia applications, New Dehli, India, 102-106.
[7] M. Engin, S. Demirag, (2003) “Fuzzy-hybrid neural network based ECG beat recognition using three different types of feature sets,” Cardiovasc. Eng. Int. J. 3 (2) 71-80.
[8] S. Hykin, (1994) Neural Networks: A comperhensive Foundation, Macmillan, New York.
[9] B. Karlik, m.o. Tokhi, M. Alci, (2003) “A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis,” IEEE Trans. Biomed. Eng. 50 (11), 1255-1261.
[10] R. Acharya, P.S. Bhat, S.S. Iyengar, A. Roo, S. Dua, (2001) “Classification of heart rate data using artificial neural network and fuzzy equivalence relation,” J. Pattern Recognition Soc, 4, 238-244.
[11] G. A. Carpenter, S. Grossberg, and D. B. Rosen, (2001) “Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive adaptive resonance system,” Neural Networks, 4, 759-771.
[12] G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, (2002) “Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps,” IEEE Trans. Neural Networks, vol. 3, pp. 698-712.

  
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