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Abstract. . .  and SE mean of 73%. Once these parameters are fixed, the performances obtained are : a specificity of 90% and a sensitivity of 84.7%. With the same training base, a discriminant analysis or a neural network leads to SP=65% and SE=70% [5]. VI. CONCLUSION In this paper, SVM is applied to detect patients prone to to atrial fibrillation . The classification results are promising: 90% of specificity and 84.7% of sensitivity that represents an increase of more 20% compared to two other classic methods : discriminant analysis and neural network.  . . .
. . .  obtained are : a specificity of 90% and a sensitivity of 84.7%. With the same training base, a discriminant analysis or a neural network leads to SP=65% and SE=70% [5]. VI. CONCLUSION In this paper, SVM is applied to detect patients prone to to atrial fibrillation . The classification results are promising: 90% of specificity and 84.7% of sensitivity that represents an increase of more 20% compared to two other classic methods : discriminant analysis and neural network. R EFERENCES [1] W.B. Kanel, R.D. Abbott, D.D.Savage, P.M. McNamara, . . .
. . .  p 105-109, 4-6 sept.2003 [7] H.Rix, and J.P. Malenge, Detecting small variations in shape, IEEE Trans , Pattern Anal. Mach. Intell, 13, pp. 252-264, 1980. [8] R.Lepage, J.M. Boucher, J.J Blanc, J.C Cornily " ECG segmentation and P-wave feature extraction: application to patients prone to atrial fibrillation , 23 Annual. Intern Conf of IEEE Medecine and Biology Society . Istanbul, vol 1, pp. 298-302, 2001. [9] D. Lemire, C. Pharand, J.C. Rajaonah, B.Dube and A.R. Leblanc, Wavelet time entropy, T-wave morphology and myocardial ischemia,  . . .
. . .  105-109, 4-6 sept.2003 [7] H.Rix, and J.P. Malenge, Detecting small variations in shape, IEEE Trans , Pattern Anal. Mach. Intell, 13, pp. 252-264, 1980. [8] R.Lepage, J.M. Boucher, J.J Blanc, J.C Cornily " ECG segmentation and P-wave feature extraction: application to patients prone to atrial fibrillation , 23 Annual. Intern Conf of IEEE Medecine and Biology Society . Istanbul, vol 1, pp. 298-302, 2001. [9] D. Lemire, C. Pharand, J.C. Rajaonah, B.Dube and A.R. Leblanc, Wavelet time entropy, T-wave morphology and myocardial ischemia, IEEE Trans. . . .
. . .  sept.2003 [7] H.Rix, and J.P. Malenge, Detecting small variations in shape, IEEE Trans , Pattern Anal. Mach. Intell, 13, pp. 252-264, 1980. [8] R.Lepage, J.M. Boucher, J.J Blanc, J.C Cornily " ECG segmentation and P-wave feature extraction: application to patients prone to atrial fibrillation , 23 Annual. Intern Conf of IEEE Medecine and Biology Society . Istanbul, vol 1, pp. 298-302, 2001. [9] D. Lemire, C. Pharand, J.C. Rajaonah, B.Dube and A.R. Leblanc, Wavelet time entropy, T-wave morphology and myocardial ischemia, IEEE Trans. Biomed.Eng . . .
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SVM Classification of patients prone to atrial fibrillation

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