References

Ahmed, N. and T. Natarajan, 1983. Discrete-Time Signals and Systems. Reston Publishing Co. Inc., Virginia, Pages: 398.

Algazi, M. and P. Meurin, 2000. L'electrocardiogramme: Guide pratique. Biopharma, Paris, pp: 248.

Atoui, H., J. Fayn and P. Rubel, 2004. A neural network approach for patient-specific 12-lead ECG synthesis in patient monitoring environments. Proceedings of the Computers in Cardiology, September 19-22, 2004, France, pp: 161-164.

Babikier, M.A., M. Izzeldin, I.M. Ishag, D.G. Lee and K.H. Ryu, 2011. Classification of cardiac arrhythmias using machine learning techniques based on ECG signal matching. http://sustech.edu/staff_publications/20111215103300603.pdf.

Bilgin, A., M.W. Marcellin and M.I. Altbach, 2003. Compression of electrocardiogram signals using JPEG2000. IEEE Trans. Consumer Electron., 49: 833-840.
CrossRef  |  

Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford University Press, UK., pp: 116-149.

Blu, T. and M. Unser, 2002. Wavelets, fractals and radial basis functions. IEEE Trans. Signal Process., 50: 543-553.
CrossRef  |  

Broomhead, D.S and D. Lowe, 1988. Multivariable functional interpolation and adaptive networks. Complex Syst., 2: 321-355.
Direct Link  |  

Ceylan, R. and Y. Ozbay, 2007. Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network. Expert Syst. Applic., 33: 286-295.
CrossRef  |  Direct Link  |  

Che, Z.G., T.A. Chiang and Z.H. Che, 2011. Feed-forward neural networks training: A comparison between genetic algorithm and back-propagation learning algorithm. Int. J. Innov. Comput. Inf., 7: 5839-5850.
Direct Link  |  

De Chazal, P., C. Heneghan, E. Sheridan, R. Reilly, P. Nolan and M. O'Malley, 2003. Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans. Biomed. Eng., 50: 686-696.
CrossRef  |  

De Lannoy, G., D. Francois, J. Delbeke and M. Verleysen, 2012. Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans. Biomed. Eng., 59: 241-247.
CrossRef  |  PubMed  |  Direct Link  |  

Fira, C.M. and L. Goras, 2008. An ECG signals compression method and its validation using NN's. IEEE Trans. Bio Med. Eng., 5: 1319-1326.
CrossRef  |  PubMed  |  

Friesen, G.M., T.C. Jannett, M.A. Jadallah, S.L. Yates, S.R. Quint and H.T. Nagle, 1990. A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans. Biomed. Eng., 37: 85-98.
PubMed  |  

Ge, D., N. Srinivasan and S.M. Krishnan, 2002. Cardiac arrhythmia classification using autoregressive modeling. Biomed. Eng. Online. 10.1186/1475-925X-1-5

Guarnieri, S., F. Piazza and A. Uncini, 1995. Multilayer neural networks with adaptive spline-based activation functions. Proceedings of the International Neural Network Society Annual Meet, July 17-21, 1995, Washington, DC., pp: I695-I699.

Haykin, S., 2001. Nonlinear Dynamical Systems: Feedforward Neural Networks: An Introduction. Wiley, USA., pp: 1-16.

Ileana, I., C. Rotar and A. Incze, 2004. The optimization of feed forward neural networks structure using genetic algorithms. Proceedings of the International Conference on Theory and Applications of Mathematics and Informatics, September 15-17, 2004, Thessaloniki, Greece, pp: 223-234.

Ince, T., S. Kiranyaz and M. Gabbouj, 2009. A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans. Biomed. Eng., 56: 1415-1426.
CrossRef  |  

Jadhav, S.M., S.L. Nalbalwar and A.A. Ghatol, 2010. Arrhythmia disease classification using artificial neural network model. Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research, December 28-29, 2010, Coimbatore, India, pp: 1-4.

Jadhav, S.M., S.L. Nalbalwar and A.A. Ghatol, 2011. Modular neural network based arrhythmia classification system using ECG signal data. Int. J. Inform. Technol. Knowl. Manage., 4: 205-209.
Direct Link  |  

Jewajinda, Y. and P. Chongstitvatana, 2010. FPGA-based online-learning using parallel genetic algorithm and neural network for ECG signal classification. Proceedings of the International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology, May 19-21, 2010, Chaing Mai, Thailand, pp: 1050-1054.

Kampouraki, A., G. Manis and C. Nikou, 2009. Heartbeat time series classification with support vector machines. IEEE Trans. Inform. Technol. Biomed., 13: 512-518.
CrossRef  |  

Karpagachelvi, S., M. Arthanari and M. Sivakumar, 2010. ECG feature extraction techniques-A survey approach. http://arxiv.org/abs/1005.0957.

Kim, H., R.F. Yazicioglu, P. Merken, C. Van Hoof and H.J. Yoo, 2010. ECG signal compression and classification algorithm with quad level vector for ECG holter system. IEEE Trans. Inform. Technol. Biomed., 14: 93-100.
CrossRef  |  

Kim, J., H.S. Shin, K. Shin and M. Lee, 2009. Robust algorithm for arrhythmia classification in ECG using extreme learning machine. Biomed. Eng. Online, Vol. 8 10.1186/1475-925X-8-31

Lee, D.H., J.W. Park, J. Choi, A. Rabbi and R. Fazel-Rezai, 2013. Automatic Detection of electrocardiogram ST segment: Application in ischemic disease diagnosis. Int. J. Adv. Comput. Sci. Appl., 4: 150-155.

Lin, C.W., Y.T. Yang, J.S. Wang and Y.C. Yang, 2012. A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation. IEEE Trans. Inform. Technol. Biomed., 16: 991-998.
CrossRef  |  

Lippmann, R., 1987. An introduction to computing with neural nets. IEEE ASSP Mag., 4: 4-22.
CrossRef  |  Direct Link  |  

Llamedo, M. and J.P. Martinez, 2011. Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng., 58: 616-625.
CrossRef  |  

McSharry, P.E., G.D. Clifford, L. Tarassenko and L.A. Smith, 2003. A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng., 50: 289-294.
CrossRef  |  Direct Link  |  

Moody, G.B. and R.G. Mark, 2001. The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol., 20: 45-50.
PubMed  |  

Pang, S., 2005. Credit scoring model based on radial basis function network. Proceedings of IEEE International Workshop on VLSl Design and Video Technology, May 28-30, 2005, China, pp: 389-392.

Pasolli, E. and F. Melgani, 2010. Active learning methods for electrocardiographic signal classification. Inform. Technol. Biomed. Trans., 14: 1405-1416.
CrossRef  |  Direct Link  |  

PhysioNet, 2010. MIT-BIH arrhythmia database. www.physionet.org/physiobank/database/mitdb.

Ramli, A.B. and P.A. Ahmad, 2003. Correlation analysis for abnormal ECG signal features extraction. Proceedings of the 4th National Conference on Telecommunication Technology, January 14-15, 2003, Malaysia, pp: 232-237.

Raut, R.D. and S.V. Dudul, 2008. Arrhythmias classification with MLP neural network and statistical analysis. Proceedings of the 1st International Conference on Emerging Trends in Engineering and Technology, July 16-18, 2008, Nagpur, India, pp: 553-558.

Schuck, A. and J.O. Wisbeck, 2003. QRS detector pre-processing using the complex wavelet transform. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Volume 3, September 17-21, 2003, Mexico, pp: 2590-2593.

Singh, B.N. and A.K. Tiwari, 2006. Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process., 16: 275-287.
CrossRef  |  Direct Link  |  

Tsipouras, M.G., D.I. Fotiadis and D. Sideris, 2005. An arrhythmia classification system based on the RR-interval signal. Artif. Intell. Med., 33: 237-250.
CrossRef  |  Direct Link  |  

Vecci, L., F. Piazza and A. Uncini, 1998. Learning and approximation capabilities of adaptive spline activation function neural networks. Neural Networks, 11: 259-270.
CrossRef  |  Direct Link  |  

Wang, Y. and Y. Jia, 2004. Face recognition based on Kernel radial basis function networks. Proceedings of the Asian Conference on Computer Vision, January 27-30, 2004, Korea, pp: 174-179.

Wen, C., M.F. Yeh and K.C. Chang, 2007. ECG beat classification using GreyART network. IET Signal Process., 1: 19-28.
CrossRef  |  

Ye, C., B.V. Kumar and M.T. Coimbra, 2012. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans. Biomed. Eng., 59: 2930-2941.
CrossRef  |  

Yu, S.N. and Y.H. Chen, 2007. Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recogn. Lett., 28: 1142-1150.
CrossRef  |