Cardiac arrhythmias classification using artificial metaplasticity algorithm

Electrocardiogram (ECG) arrhythmias such as ventricular and atrial arrhythmias are one of the common causes of death. These abnormalities of heart activity may cause immediate death or damage to the heart. If the abnormal symptoms can be detected and diagnosed early, time is saved to prevent the occurrence of heart attack. Therefore, it is necessary to have an effective method for early detection and early treatment. We propose, in this paper, an intelligent method to accurately classify the heartbeat of ECG signals through the Artificial Metaplasticity Multilayer Perceptron (AMMLP). The MIT-BIH database is used to classify arrhythmias into three different types: Premature Ventricular Contraction (PVC), Right Bundle Branch Block (RBBB) and Left Bundle Branch Block (LBBB); normal ECG signals are also used in the study. 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.

[1]  Nibedit Dey,et al.  ECG signal denoising by Functional Link Artificial Neural Network (FLANN) , 2011 .

[2]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[3]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[4]  J. Miguel Barrón-Adame,et al.  Error Weighting in Artificial Neural Networks Learning Interpreted as a Metaplasticity Model , 2007, IWINAC.

[5]  Javier Ropero Peláez,et al.  Biological Clues for Up-to-Date Artificial Neurons , 2007 .

[6]  Stanislaw Osowski,et al.  ECG beat recognition using fuzzy hybrid neural network , 2001, IEEE Trans. Biomed. Eng..

[7]  Henry Leung,et al.  The complex backpropagation algorithm , 1991, IEEE Trans. Signal Process..

[8]  Martin T. Hagan,et al.  Neural network design , 1995 .

[9]  Yasmine Benchaib.  A SPECIALIZED LEARNING FOR NEURAL CLASSIFICATION OF CARDIAC ARRHYTHMIAS , 2009 .

[10]  M. Bear,et al.  Metaplasticity: the plasticity of synaptic plasticity , 1996, Trends in Neurosciences.

[11]  Anupam Shukla,et al.  Diagnosis of breast cancer by modular evolutionary neural networks , 2011 .

[12]  Kemal Polat,et al.  A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia , 2006, Expert Syst. Appl..

[13]  Haiying Zhou,et al.  A New System Dedicated to Real-time Cardiac Arrhythmias Tele-assistance and Monitoring , 2006, J. Univers. Comput. Sci..

[14]  Chandrakar Kamath.  ECG classification using morphological features derived from symbolic dynamics , 2012 .

[15]  Duc Truong Pham,et al.  Computational Intelligence: for Engineering and Manufacturing , 2007 .

[16]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[17]  J. S. Sahambi,et al.  Classification of ECG arrhythmias using multi-resolution analysis and neural networks , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[18]  R. Kumar,et al.  Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network , 2011 .

[19]  Emilio Del-Moral-Hernandez,et al.  A Preliminary Neural Model for Movement Direction Recognition Based on Biologically Plausible Plasticity Rules , 2007, IWINAC.

[20]  Diego Andina,et al.  Artificial Metaplasticity can Improve Artificial Neural Networks Learning , 2013, Intell. Autom. Soft Comput..

[21]  D. Andina,et al.  Using neural networks to simulate the Alzheimer’s Disease , 2008, 2008 World Automation Congress.

[22]  D. Andina,et al.  Wood defects classification using Artificial Metaplasticity neural network , 2009, 2009 35th Annual Conference of IEEE Industrial Electronics.

[23]  M. I. Owis,et al.  Characterisation of electrocardiogram signals based on blind source separation , 2002, Medical and Biological Engineering and Computing.

[24]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[25]  Sung-Nien Yu,et al.  Integration of independent component analysis and neural networks for ECG beat classification , 2008, Expert Syst. Appl..

[26]  Wickliffe C. Abraham,et al.  Metaplasticity: Key Element in Memory and Learning? , 1999, News in physiological sciences : an international journal of physiology produced jointly by the International Union of Physiological Sciences and the American Physiological Society.

[27]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[28]  P. Jedlicka,et al.  Synaptic plasticity, metaplasticity and BCM theory. , 2002, Bratislavske lekarske listy.

[29]  C. Mathers,et al.  Projections of Global Mortality and Burden of Disease from 2002 to 2030 , 2006, PLoS medicine.

[30]  Sanjay L. Nalbalwar,et al.  Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data , 2012 .

[31]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[32]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[33]  Joel Quintanilla-Domínguez,et al.  Breast cancer classification applying artificial metaplasticity algorithm , 2011, Neurocomputing.

[34]  Antonio Malgaroli,et al.  LTP and adaptation to inactivity: Overlapping mechanisms and implications for metaplasticity , 2007, Neuropharmacology.

[35]  Mohamed Yacin Sikkandar,et al.  Analysis of cardiac abnormalities using Hilbert-Huang transform , 2013 .

[36]  G. Lyons,et al.  Bayesian ANN classifier for ECG arrhythmia diagnostic system: a comparison study , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..