References
- Y. H. Hu, S. Palreddy, and W. Tomkins, 'A patient adaptable ECG beat classification using a mixture of experts approach,' IEEE Trans. Biomed. Eng., vol. 44, no. 9, pp. 891-900, September 1997 https://doi.org/10.1109/10.623058
- Y. H. Hu, W. Tomkins, J. L. Urrusti, and V. X. Alfonso, 'Applications of artificial neural networks for ECG signal detection and classification,' Electrocardiology, vol. 24, pp. 123-129, 1994
- K. Minami, H. Nakajima, and T. Yoyoshima, 'Real time discrimination of the ventricular tachyarrhythmia with Fourier-transform neural network,' IEEE Trans. Biomed. Eng., vol. 46, no. 2, pp. 179-185, 1999 https://doi.org/10.1109/10.740880
- G. E. Oien, N. A. Bertelsen, T. Eftestol, and J. H. Husoy, 'ECG rhythm classification using artificial neural networks,' Proc. of the IEEE Digital Signal Processing Workshop, pp. 514- 517, 1996
- T. Sugiura, H. Hirata, Y. Harada, and T. Kazui, 'Automatic discrimination of arrhythmia waveforms using fuzzy logic,' Proc. of the IEEE Engineering in Medical and Biology Society, vol. 20, no. 1, pp. 108-111, 1998
- L. Y. Shyu, Y. H. Wu, and W. Hu, 'Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG,' IEEE Trans. Biomed. Eng., vol. 51, no. 7, pp. 1269-1273, 2004 https://doi.org/10.1109/TBME.2004.824131
- R. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches, Wiley, New York ,1992
- S. Kadambe, R. Murray, and G. F. B. Bartels, 'Wavelet transform-based QRS complex detector,' IEEE Trans. Biomed. Eng., vol. 46, no. 7, pp. 838-848, July 1999 https://doi.org/10.1109/10.771194
- K. L. Park, K. J. Lee, and H. R. Yoon, 'Application of a wavelet adaptive filter to minimize distortion of the ST-segment,' Med. And Biol. Eng. and Computing, vol. 36, no. 5, pp. 581-586, 1998 https://doi.org/10.1007/BF02524427
- H. C. Kim, D. J. Kim, and S. Y. Bang, 'Face recognition using LDA mixture model,' Proc. of the Pattern Recognition, vol. 2, pp. 925-928, 2002
- A. M. Martinez and A. C. Kak, 'PCA versus LDA,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, 2001 https://doi.org/10.1109/34.908974
- A. Smola and B. Scholkopf, 'A tutorial on support vector regression,' NeuroColt Tech. Rep. NV2-TR-1998-030, Royal Holloway College, Univ. London, London, U. K., 1998
- O. L. Mangasarian, 'Lagrangian support vector machines,' J. Machine Learning Res., vol. 1, pp. 161-177, 2001 https://doi.org/10.1162/15324430152748218
- J. Platt, 'Fast training of SVM using sequential optimization,' Advances in Kernel Methods- Support Vector Learning, B. Scholkpf, C. Burges, and A. Smola, Eds. Cambridge, MIT Press, U.K., pp. 185-208, 1998
- C. Burges, 'A tutorial on support vector machines for pattern recognition,' Knowledge Discovery and Data Mining, U. Fayyad, Ed. Norwell, Kluwer, MA, pp. 1-43, 2000
- K. Crammer and Y. Singer, 'On the learn ability and design of output codes for multi-class problems,' Proc. 13th Conf. Computational Learning Theory, pp. 35-46, 2000
- C. W. Hsu and C. J. Lin, A Comparison of Methods for Mmulti-class Support Vector Machines, Nat. Taiwan Univ., Taiwan, 2000