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[1] Emanet, N 2009 ECG beat classification by using discrete wavelet transform and Random Forest algorithm Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control(ICSCCW 2009),Fifth International Conference (Famagusta, 2-4 September 2009) pp. 1–4 [2] Vijaya, V, Rao, K K and Rama,V 2011 Arrhythmia Detection through ECG Feature Extraction using Wavelet Analysis European Journal of Scientific Research 66 (3) 441–48 [3] Hampoton, J R 2003The ECG Made Easy. 6th GH: Churchill Livingstone pp1–10 [4] Adib, A and Haque, MA 2010 ECG beat classification using discrete wavelet coefficients Health Informatics and Bioinformatics (HIBIT), 5th International Symposium 1–6 [5] Sadaphule, M M, Mule, S B and Rajankar, S O 2012 ECG Analysis Using Wavelet Transform and Neural Network International Journal of Engineering Inventions 1(12)1–7 [6] Physiobank Archieve Index 2013 The European ST-T Database, http://www.physionet.org/physiobank/database/edb. [7] Froese, T, Hadjiloucas, S, Galvao, R K H, Becerra V M and Coelho, C J 2006 Comparison of extrasystolic ECG signal classifiers using Discrete Wavelet Transforms, Pattern Recognition Letters 27(5) 393-407. [8] Ubeyli, E D 2009 Combined neural network model employing wavelet coefficients for EEG signals classification Digital Signal Processing. 19 (2) 297-308. [9] Yeh Y C, Wang W J and Chiou C W 2009 Cardiac Arrhythmia Diagnosis Method Using Linear Discriminant Analysis on ECG Signals Measurement 42 778–89. [10] Yeap T H, Johnson F, and Rachniowski M, ‘ECG Beat Classification by a Neural Network Proceedings Annual International Conference of the IEEE EMBS Society, pp.167–173, 1990. [11] Palreddy S, Tompkins W J, and Hu Y H 1995 Customization of ECG Beat Classifiers Developed using SOM and LVQ Engineering in Med And Biol Soc. [12] Ulbeyli E D 2007 ECG Beats Classification Using Multiclass Support Vector Machines with Error Correcting Output Codes Digital Signal Processing 17 675–684. [13] Özbay Y Ceylan R and Karlik B A 2006 Fuzzy Clustering Neural Network Architecture for Classification of ECG Arrhythmias Comput Biol Med. 36 376–88. [14] Ulbeyli E D 2010 Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals Expert Systems with Applications 37 1192–99. [15] Ulbeyli E D 2009 Combining recurrent neural networks with eigenvector methods for classification of ECG beats Digital Signal Processing 19 320–29. [16] Ulbeyli E D 2008 Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients Computers in Biology and Medicine 38 401–10. [17] Gallagher J C, Boddhu, S K and Vigraham, S 2005 A Reconfigurable Continuous Time Recurrent Neural Network for Evolvable Hardware Applications. IEEE Computer Society NASA/DoD Conference of Evolution Hardware (EH’05) 247–20. [18] Fiore, J M and Gallagher, J C 2004 Continuous time recurrent neural networks: a paradigm for evolvable analog controller circuits IEEE National Aerospace and Electronics Conference (NAECON) 2000, 299–304. [19] Al Seyab, R K and Cao, Y 2008 Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation, Journal of Process Control, 18 568–81.