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High Efficient System for Automatic Classification of the Electrocardiogram Beats

1. Acharya, U. R., M. Sankaranarayanan, J. Nayak, C. Xiang, and T. Tamura. Automatic identification of cardiac health using modeling techniques: a comparative study. Inform. Sci. 178:4571–4582, 2008.

   Article  Google Scholar 

2. Andreao, R. V., B. Dorizzi, and J. Boudy. ECG signal analysis through hidden Markov models. IEEE Trans. Biomed. Eng. 53:1541–1549, 2006.

   Article  PubMed  Google Scholar 

3. Bandyopadhyay, S., and S. K. Pal. Classification and Learning Using Genetic Algorithms. Berlin, Heidelberg: Springer-Verlag, 2007.

   Google Scholar 

4. Burges, C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2:121–167, 1998.

   Article  Google Scholar 

5. Chazal, P., M. O’Dwyer, and R. B. Reilly. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51:1196–1206, 2004.

   Article  PubMed  Google Scholar 

6. Clifford, G. D., F. Azuaje, and P. E. McShary. Advanced Methods and Tools for ECG Data Analysis. Norwood, MA: Artech House, 2006.

   Google Scholar 

7. de Chazal, F., and R. B. Reilly. A patient adapting heart beat classifier using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 53:2535–2543, 2006.

   Article  PubMed  Google Scholar 

8. Ebrahimzadeh, A., and A. Khazaee. Detection of premature ventricular contractions using MLP neural networks: a comparative study. Measurement 43(1):103–112, 2009.

   Article  Google Scholar 

9. Ebrahimzadeh, A., A. Khazaee, and V. Ranaee. Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput. Methods Programs Biomed. 99:179–194, 2010.

   Article  Google Scholar 

10. Hsu, W. C., and C. J. Lin. A simple decomposition method for support vector machine. Mach. Learn. 46:219–314, 2002.

    Article  Google Scholar 

11. Huang, C., and C. Wang. A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31:231–240, 2006.

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

13. JoyMartis, R., C. Chakraborty, and A. K. Ray. A two-stage mechanism for registration and classification of ECG using Gaussian mixture model. Pattern Recognit. 42:2979–2988, 2009.

    Article  Google Scholar 

14. Khadra, L., A. S. Al-Fahoum, and S. Binajjaj. A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques. IEEE Trans. Biomed. Eng. 52:1840–1845, 2005.

    Article  PubMed  Google Scholar 

15. Lagerholm, M., C. Peterson, G. Braccini, L. Edenbrandt, and L. Sornmo. Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 47:839–847, 2000.

    Article  Google Scholar 

16. Lin, C. H. Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier. Comput. Math. Appl. 55:680–690, 2008.

    Article  Google Scholar 

17. Mallat, S. A Wavelet Tour of Signal Processing. London: Academic Press, 2002.

    Google Scholar 

18. Mark, R. G., and G. B. Moody. MIT-BIH Arrhythmia Database, 1997 [Online]. Available: http://ecg.mit.edu/dbinfo.html.

19. Michalewicz, Z. Genetic Algorithms + Data Structures = Evolution Programs (3rd ed.). New York, NY: Springer, 1999.

    Google Scholar 

20. Misiti, M., Y. Misiti, G. Oppenheim, and J. Poggi. Wavelet Toolbox User’s Guide. Natick: The MathWorks, Inc., 2007.

    Google Scholar 

21. Mitra, S., M. Mitra, and B. B. Chaudhuri. A rough set-based inference engine for ECG classification. IEEE Trans. Instrum. Meas. 55:2198–2206, 2006.

    Article  Google Scholar 

22. Mohammadzadeh Asl, B., S. K. Setarehdan, and M. Mohebbi. Support vector machine-based arrhythmia classification using reduced features of heart rate variability. Artif. Intell. Med. 44:51–64, 2008.

    Article  Google Scholar 

23. Moody, G. B., and R. G. Mark. The impact of the MIT/BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3):45–50, 2001.

    Article  CAS  PubMed  Google Scholar 

24. Osowski, S., T. Markiewicz, and L. T. Hoai. Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement 41:610–617, 2008.

    Article  Google Scholar 

25. Sarvestani, R. R., R. Boostani, and M. Roopaei. VT and VF classification using trajectory analysis. Nonlinear Anal. 2008. doi:10.1016/j.na.2008.10.015.

26. Shyu, L. Y., Y. H. Wu, and W. C. Hu. Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG. IEEE Trans. Biomed. Eng. 51:1269–1273, 2004.

    Article  PubMed  Google Scholar 

27. Sumathi, S., T. Hamsapriya, and P. Surekha. Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab. Berlin, Heidelberg: Springer-Verlag, 2008.

    Google Scholar 

28. Ubeyli, E. D. Support vector machines for detection of electrocardiographic changes in partial epileptic. Eng. Appl. Artif. Intell. 21:1196–1203, 2008.

    Article  Google Scholar 

29. Vapnik, V. Statistical Learning Theory. New York: Wiley, 1998.

    Google Scholar 

30. Wu, C., G. Tzeng, Y. Goo, and W. Fang. A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst. Appl. 32:397–408, 2007.

    Article  Google Scholar 

31. Yu, S. N., and K. T. Chou. Selection of significant for ECG beat classification. Expert Syst. Appl. 36:2088–2096, 2009.

    Article  Google Scholar 

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