S. Levy, G. Breithardt, R. W. Campbell, A. J. Camm, J. C. Daubert et al., Atrial fibrillation: current knowledge and recommendations for management. Working Group of Arrhythmias of the European Society of Cardiology, European Heart Journal, vol.19, pp.1294-320, 1998.

Z. Haddi, J. F. Pons, S. Delliaux, B. Ananou, J. C. Deharo et al., A Robust Detection Method of Short Atrial Fibrillation Episodes, Computing in Cardiology, vol.44, pp.1-4, 2017.

R. Xiuhua, L. Changchun, L. Chengyu, W. Xinpei, and L. Peng, Automatic detection of atrial fibrillation using R-R interval signal, BMEI, issue.2, pp.644-647, 2011.

J. S. Richman and J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, American Journal of Physiology, Heart and Circulatory Physiology, vol.278, p.2039, 2000.

D. E. Lake and J. R. Moorman, Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices, Am J Physiol Heart Circ Physiol, vol.300, pp.319-344, 2011.

Z. M. Hira and D. F. Gillies, A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data, Adv Bioinformatics, 2015.

G. Lanckriet, N. Cristianini, P. Bartlett, and L. E. Ghaoui, Learning the kernel matrix with semi-definite programming, J. Mach. Learn. Res, vol.5, pp.27-72, 2004.

S. Sonnenburg, G. Rätsch, and C. Schäfer, A general and efficient multiple kernel learning algorithm, NIPS, pp.1273-1280, 2006.

M. Kloft, U. Brefeld, S. Sonnenburg, and A. Zien, lp-Norm multiple kernel learning, J. Mach. Learn. Res, vol.12, pp.953-997, 2011.

Z. Xu, R. Jin, S. Zhu, M. Lyu, and I. King, Smooth optimization for effective multiple kernel learning, Proc. AAAI Artif. Intell, 2010.

M. Kowalski, M. Szafranski, and L. Ralaivola, Multiple indefinite kernel learning with mixed norm regularization, Proc. 26th ICML, pp.545-552, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00424033

A. Rakotomamonjy, F. Bach, S. Canu, Y. Grandvalet, and S. , Journal of Machine Learning Research, Journal of Machine Learning Research, vol.9, p.2491, 2008.

J. F. Pons, Z. Haddi, J. C. Deharo, A. Charaï, R. Bouchakour et al.,

&. S. Ouladsine and . Delliaux, Heart rhythm characterization through induced physiological variables, Scientific Reports, vol.7, p.5059, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01760784

S. Lu, X. Chen, J. K. Kanters, . Ic, K. H. Solomon et al., Automatic selection of the threshold value R for approximate entropy, IEEE Trans Biomed Eng, vol.55, pp.1966-72, 2008.

S. M. Pincus, Approximate Entropy as a Measure of System Complexity, Proc. Natl Academy Sci. USA, vol.88, pp.2297-2301, 1991.

C. Weiting, W. Zhizhong, X. Hongbo, and Y. Wangxin, Characterization of Surface EMG Signal Based on Fuzzy Entropy, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.15, 2007.

C. Cortes and V. Vapnik, Support Vector Networks, Machine Learning, vol.20, pp.273-297, 1995.

. B. Boser, .. I. Guyon, and . Vapnik, V, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory-COLT '92, p.144