Abstract
The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a fuzzy C-mean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats. The performance has been compared with FCM clustered multi layered feed forward network (MLFFN) trained with back propagation algorithm. Important parameters are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis using the MIT-BIH arrhythmia database has shown an average classification accuracy of 97.54% with FCM clustered MLFFN and 99.58% with FCM clustered PNN. Fuzzy clustering improves the classification speed as well. The result reveals the capability of the FCM clustered PNN in the computer-aided diagnosis of ECG abnormalities.
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References
Rajendra Acharya, U., Subhanna Bhat, P., Iyengar, S. S., Rao, A., and Dua, S., Classification of heart rate data using artificial neural network and fuzzy equivalence relation. J. Pattern Recogn. 36 (1)61–68, 2003.
Reddy, D. C., Biomedical signal processing principles and techniques. McGraw-Hill, New Delhi, p. 255, 2005.
Caswell, S. A., Kluge, K. S., Chiang, C. M. J., and Jenkins, J. M., Pattern recognition of cardiac arrhythmias using two intracardiac channels. In: Proceedings of Computers in Cardiology. London, UK, pp. 181–184, 1993.
Thakor, N. V., Natarajan, A., and Tomaselli, G. F., Multi way sequential hypothesis testing for tachyarrhythmia discrimination. IEEE Trans. Biomed. Eng. 41 (5)480–487, 1994.
Afonoso, V. X., and Tompkins, W. J., Detecting ventricular fibrillation: Selecting the appropriate time- frequency analysis tool for the application. IEEE Eng. Med. Biol. Mag. 14 (2)152–159, 1995.
Finelli, C. J., The time sequenced adaptive filter for analysis of cardiac arrhythmias in intraventricular electrocardiograms. IEEE Trans. Biomed. Eng. 43 (8)811–819, 1996.
Senhadji, L., Carrault, G., Bellanger, J. J., and Passariello, G., Comparing wavelet transforms for recognizing cardiac patterns. IEEE. Eng. Med. Biol. 14 (2)167–173, 1995.
Khadra, L., Al-Fahoum, A. S., and Al-Nashash, H., Detection of life threatening cardiac arrhythmias using the wavelet transformation. Med. Biol. Eng. Comput. 35 (6)626–632, 1997.
Tian, L., and Tompinks, W. J., Time domain based algorithm for detection of ventricular fibrillation. In: Proceedings of EMBS 19th Annual International Conference, Chicago, pp. 374–377, 1997.
Zhang, X. S., Zhu, Y. S., Thakor, N. V., and Wang, Z. Z., Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans. Biomed. Eng. 46 (5)548–555, 1999.
Chen, S. W., A Two stage discrimination of cardiac arrhythmia using a total least squre-based prony modeling algorithm. IEEE Trans. Biomed. Eng. 47 (10)1317–1327, 2000.
Owis, M. I., Abou-Zied, Youssef, A., and Kadah, Y. M., Study of features based on nonlinear dynamic modeling in ECG arrhythmia detection and classification. IEEE Trans. Biomed. Eng. 49 (7)733–736, 2002.
Hu, Y. H., Tompkins, W. J., Urrusti, J. L., and Alfonso, V. X., Applications of artificial neural networks for ECG signal detection and classification. J. Electrocard. 28 (supplement)66–73, 1994.
Silipo, R., and Marchesi, C., Artificial neural networks for automatic ECG analysis. IEEE Trans. Signal Process. 46 (5)1417–1425, 1998.
Melo, S. L., Caloba, L. P., and Nadal, J., Arrhythmia analysis using artificial neural networks and decimated electrocardiographic data. Comput. Cardio. 27:73–76, 2000.
Issac Niwas, S., Shantha Selva Kumari, R., and Sadasivam, V., Artificial neural network based automatic cardiac abnormalities classification. In: Proceedings of 6th International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), pp. 41–46, 2005.
Haykin, S., Neural Networks: A comprehensive foundation. Macmillan, New York, 1994.
Al-Fahoum, A. S., and Howitt, I., Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias. Med. Biol. Eng. Comput. 37 (1)566–573, 1999.
Minami, K., Nakajima, H., and Toyoshima, T., Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans. Biomed. Eng. 46 (2)179–185, 1999.
Osowski, S., and Linh, T. H., ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48 (11)1265–1271, 2001.
Dokur, Z., and Olmez, T., ECG beat classification by a hybrid neural network. Comp. Methods Progr. Biomed. 66:167–181, 2001.
Engin, M., and Demirag, S., Fuzzy- hybrid neural network based ECG beat recognition using three different types of feature sets. Cardiovasc. Eng. Int. J. 3 (2)71–80, 2003.
Ceylan, R., and Ozbay, Y., Comparison of FCM, PCA and WT techniques for classification of ECG arrhythmias using artificial neural network. Expert Syst. Appl. 33 (2)286–295, 2007.
Ozbay, Y., Ceylan, R., and Karlik, B., A fuzzy clustering neural network architecture for classification of ECG arrhythmia. Comput. Biol. Med. 36:376–388, 2006.
Yu, S.-N., and Chen, Y.-H., Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recogn. Lett. 28 (10)1142–1150, 2007.
Jayachandran, E., Joseph, P. K., and Rajendra Acharya,U., Analysis of myocardial infraction using discrete wavelet transform. J. Med. Syst., 2009 doi:10.1007/s10916-009-9314-5.
Yu, S.-N., and Chou, K.-T., Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst. Appl. 34:2841–2846, 2008.
Heu Hu, Y., Palreddy, S., and Tompkins, W. J., A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans. Biomed. Eng. 44 (9)891–900, 1997.
MacQueen, J. B., Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press, Berkeley, pp. 281–297, 1967.
Maulik, U., and Bandyopadhyay, S., Performance evaluation of some clustering algorithm and validity indices. IEEE Trans. Pattern Anal. Mach. Intell. 24 (12)1650–1654, 2002.
Kohenen, T., Self Organizing Maps. Springer, Berlin, 1995.
Clifford, G. D., Azuaje, F., and McSharry, P. E., Advanced methods and tools for ECG data analysis. Artech House, London, 2006.
İnan, Z., and Kuntalp, M., A study on fuzzy C-means clustering-based systems in automatic spike detection. Compu. Biol. and Med. 37 (8)1160–1166, 2007.
Bezdek, J., Pattern recognition with fuzzy objective function algorithms. Plenum, USA, 1981.
Dunn, J. C., A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters. J. Cybern. 3 (3)32–57, 1974.
Nefti, S., and Oussalah, M., Probabilistic-fuzzy clustering algorithm. IEEE International Conference on Systems, Man and Cybemetics (5):4786–4791, 2004.
Alata, M., Molhim, M., and Ramini, A., Optimizing of fuzzy C-means clustering algorithm using GA. Proceedings of World Academy of Science, Engineering and Technology, vol. 39, 2008.
Ubeyli, E. D., Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals. Compt. Biol. Med. 38:82–89, 2008.
Rutkowski, L., Adaptive probabilistic neural networks for pattern classification in time varying environment. IEEE Trans. Neural Netw. 15:811–827, 2004.
Berthold, M. R., and Diamond, J., Constructive training of probabilistic neural networks. Neuro Comput. 19:167–183, 1998.
PhysioNet. The research resource for complex physiologic signals. http://www.physionet.org/.
Tan, K. F., Chan, K. L., and Choi, K., Detection of the QRS complex, P wave and T wave in Electrocardiogram. In: First International Conference on Advances in Medical Signal and Information Processing Proceedings, pp. 41–47, 2000.
Daskalov, K., Dotsinsky, I. A., and Christov, I. I., Developments in ECG acquisition, preprocessing, parameter measurement, and recording. IEEE Eng. Med. Biol. 17 (1)50–58, 1998.
Mathworks Inc., Neural Networks Toolbox User’s Guide, version.3 (Release 11), 1998.
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Haseena, H.H., Mathew, A.T. & Paul, J.K. Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification. J Med Syst 35, 179–188 (2011). https://doi.org/10.1007/s10916-009-9355-9
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DOI: https://doi.org/10.1007/s10916-009-9355-9