Abstract
Correct diagnosis of cardiac arrhythmias is one of the major problems in medical field. Cardiac arrhythmias can be early detected and diagnosed to prevent the occurrence of heart attack as well as the consequent deaths. An effective method for early detection of these arrhythmias, and thus to procure early treatment, is necessary. In this research we have applied artificial metaplasticity multilayer perceptron (AMMLP) to cardiac arrhythmias classification. The MIT-BIH Arrhythmia Database was used to train and test AMMLPs. The obtained AMMLP classification accuracy of 98.25%, is an excellent result compared to the classical MLP and recent classification techniques applied to the same database.
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References
World Health Organization. Cardiovascular diseases, http://www.euro.who.int/en/what-we-do/health-topics/noncommunicable-diseases/cardiovascular-diseases/definition
Haiying, Z.: A New System Dedicated to Real-time Cardiac Arrhythmias Tele-assistance and Monitoring. Journal of Universal Computer ScienceĀ 12(1), 30ā44 (2006)
Jadhav, S.M., Nalbalwar, S.L., Ghatol, A.A.: Artificial Neural Network Models based cardiac Arrhythmia Disease Diagnosis from ECG Signal Data. International Journal of computer ApllicationsĀ 44(15), 8ā13 (2012)
Dayong, G., Madden, M., Chambers, D., Lyons, G.: A Bayesian ANN Classifier for ECG Arrhythmia Diagnostic System: A Comparison Study. In: Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 - August 4 (2005)
Gothwal, H., Kedawat, S., Kumar, R.: Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. Journal of Biomedical Science and EngineeringĀ 4, 289ā296 (2011)
Yu, S.N., Chou, K.T.: Integration of independent component analysis and neural networks for ECG beat classification. Expert Systems with ApplicationsĀ 34(4), 2841ā2846 (2008)
Holter, N.J.: New methods for heart studies. ScienceĀ 134, 12ā14 (1961)
Fogoros, R.N.: The Electrocardiogram (ECG), http://heartdisease.about.com
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology MagazineĀ 20(3), 45ā50 (2001)
MIT-BIH arrhythmia databse, Harvard-MIT Division of Health Science Technology, Biomedical Health Centre, 1st edn., Cambridge, MA, USA, pp. 1975ā1979 (1980)
Andina, D., Pham, D.: Computational Intelligence for Engineering and Manufacturing. Springer, The Nederlands (2007)
Haykin, S.: Neural Networks a Comprehensive Foundation. MacMillan College Publishing Company, New York (1995)
Hagan, M.T., Demuth, H.B., Beale, M.: Neural network design. Thomson Learning, Stamford (1996)
Abraham, W.C.: Activity-dependent regulation of synaptic plasticity (metaplasticity) in the hippocampus. In: The Hippocampus: Functions and Clinical Relevance, pp. 15ā26. Elsevier Science, Amsterdam (1996)
Kinto, E.A., Del Moral Hernandez, E., Marcano CedeƱo, A., Ropero-PelĆ”ez, J.: A preliminary neural model for movement direction recognition based on biologically plausible plasticity rules. In: Mira, J., Ćlvarez, J.R. (eds.) IWINAC 2007. LNCS, vol.Ā 4528, pp. 628ā636. Springer, Heidelberg (2007)
Andina, D., Alvarez-Vellisco, A., Jevtic, A., Fombellida, J.: Artificial metaplasticity can improve artificial neural network learning. Intelligent Automation and Soft Computing; Special Issue in Signal Processing and Soft ComputingĀ 15(4), 681ā694 (2009)
Marcano-CedeƱo, A., Quintanilla-Dominguez, J., Andina, D.: Breast cancer classification applying artificial metaplasticity algorithm. NeurocomputingĀ 74(8), 1243ā1250 (2011)
Shannon, C.E.: A mathematical theory of communication. The Bell System Technical JournalĀ 27, 379ā423 (1948)
Leung, H., Haykin, S.: The complex backpropagation algorithm. IEEE Transactions on Signal ProcessingĀ 39(9), 2101ā2104 (1991)
Frawley, W.J., Paitetsky-Shapiro, G., Matheus, C.J.: From data mining to knowledge discovery: An overview. In: Advances in Knowledge Discovery and Data Mining, pp. 611ā620. AAAI Press/The MIT Press (1996)
Polat, K., Sahan, S., Gnes, S.: A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. Expert Systems with ApplicationsĀ 31(2), 264ā269 (2006)
Hu, Y.H., Palreddy, S., Tompkins, W.J.: A patient- adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical EngineeringĀ 44(9), 891ā900 (1997)
Minami, K., Nakajima, H., Toyoshima, T.: Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Transactions on Biomedical EngineeringĀ 46(2), 179ā185 (1999)
Osowski, S., Lin, T.H.: ECG beat recognition using fuzzy hybrid neural network. IEEE Transactions on Biomedical EngineeringĀ 48(11), 1265ā1271 (2001)
Owis, M.I., Youssef, A.B.M., Kadah, Y.M.: Characterization of ECG signals based on blind source separation. Medical and Biological Engineering and ComputingĀ 40(5), 557ā564 (2002)
Prasad, G.K., Sahambi, J.S.: Classification of ECG arrhythmias using multi-resolution analysis and neural networks. In: Proceedings of TENCON 2003 IEEE Conference on Convergent Technologies, vol.Ā 1, pp. 227ā231 (2003)
Benchaib, Y., Chikh, M.: A Specialized learning for neural classification of cardiac arrhythmias. Journal of Theoretical and Applied Information TechnologyĀ 6(1), 81ā89 (2009)
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Benchaib, Y., Marcano-CedeƱo, A., Torres-Alegre, S., Andina, D. (2013). Application of Artificial Metaplasticity Neural Networks to Cardiac Arrhythmias Classification. In: FerrĆ”ndez Vicente, J.M., Ćlvarez SĆ”nchez, J.R., de la Paz LĆ³pez, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_19
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DOI: https://doi.org/10.1007/978-3-642-38637-4_19
Publisher Name: Springer, Berlin, Heidelberg
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