C. T. January, AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology / American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society, Journal of the American College of Cardiology, vol.64, issue.21, 2014.

J. Seitz, AF ablation guided by spatiotemporal electrogram dispersion without pulmonary vein isolation: a wholly patient-tailored approach, Journal of the American College of Cardiology, vol.69, issue.3, pp.303-321, 2017.

D. I. Ellis and G. Royston, Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy, Analyst, vol.131, issue.8, pp.875-885, 2006.

F. Melgani and B. Yakoub, Classification of electrocardiogram signals with support vector machines and particle swarm optimization, IEEE Trans. on Information Technology in Biomed, vol.12, issue.5, pp.667-677, 2008.

B. Pyakillya, N. Kazachenko, and N. Mikhailovsky, Deep learning for ECG classification, Journal of Physics: Conference Series, vol.913, issue.1, 2017.

D. N. Joanes and C. A. Gill, Comparing measures of sample skewness and kurtosis, Journal of the Royal Statistical Society: Series D (The Statistician), vol.47, issue.1, pp.183-189, 1998.

C. D. Manning, R. Prabhakar, and S. Hinrich, Hierarchical clus-tering, Introduction to information retrieval, pp.378-401, 2008.

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol.14, 1995.

C. Shorten and L. K. Taghi, A survey on image data augmentation for deep learning, Journal of Big Data, vol.6, issue.1, p.60, 2019.

A. Verma, Approaches to catheter ablation for persistent atrial fibrillation, New England Journal of Medicine, vol.372, issue.19, pp.1812-1822, 2015.

S. H. Cha, Comprehensive survey on distance/similarity measures between probability density functions, City, vol.1, issue.2, p.1, 2007.

N. Srivastava, Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

M. Kuhn and J. Kjell, Applied predictive modeling, vol.26, p.70, 2013.

P. K. Diederik and B. Jimmy, Adam: A method for stochastic optimization, 2015.