I. Derivatives, The following entries are dV/dt max (maximum derivative during the upstroke), dV/dt min (minimum derivative during the repolarization) and AUC (area under curve)

]. R. References1, S. Gul, and . Bernhard, Parametric uncertainty and global sensitivity analysis in a model of the carotid bifurcation: Identification and ranking of most sensitive model parameters, Mathematical Biosciences, vol.269, pp.104-116, 2015.

A. Cintrón-arias, H. Banks, A. Capaldi, and A. L. Lloyd, A sensitivity matrix based methodology for inverse problem formulation, Journal of Inverse and Ill-posed Problems, vol.118, issue.6, pp.545-564, 2009.
DOI : 10.1002/kin.20369

C. Krier, D. Francois, V. Wertz, and M. Verleysen, FEATURE SCORING BY MUTUAL INFORMATION FOR CLASSIFICATION OF MASS SPECTRA, Applied Artificial Intelligence, p.pp. ?, 2006.
DOI : 10.1142/9789812774118_0079

J. T. Ottesen, J. Mehlsen, and M. S. Olufsen, Structural correlation method for model reduction and practical estimation of patient specific parameters illustrated on heart rate regulation, Mathematical Biosciences, vol.257, pp.257-50, 2014.
DOI : 10.1016/j.mbs.2014.07.003
URL : http://europepmc.org/articles/pmc4252605?pdf=render

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, Journal of machine learning research, vol.3, pp.1157-1182, 2003.

D. G. Arqù-es and C. J. Michel, Study of a perturbation in the coding periodicity, Mathematical Biosciences, vol.86, issue.1, pp.1-14, 1987.
DOI : 10.1016/0025-5564(87)90060-5

P. Gokulakrishnan, A. Lawrence, P. Mclellan, and E. Grandmaison, A functional-PCA approach for analyzing and reducing complex chemical mechanisms, Computers & Chemical Engineering, vol.30, issue.6-7, pp.1093-1101, 2006.
DOI : 10.1016/j.compchemeng.2006.02.007

P. G. Constantine, Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies, SIAM, 2015.
DOI : 10.1137/1.9781611973860

S. Wold, A. Ruhe, H. Wold, W. Dunn, and . Iii, The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses, SIAM Journal on Scientific and Statistical Computing, vol.5, issue.3, pp.735-743, 1984.
DOI : 10.1137/0905052

S. Young, M. E. Goddard, J. E. Pryce, and G. Deng, Kernel methods and haplotypes used in selection of sparse DNA markers for protein yield in dairy cattle, Mathematical Biosciences, vol.243, issue.1, pp.57-66, 2013.
DOI : 10.1016/j.mbs.2013.01.009

S. M. Davidson, P. D. Docherty, and R. Murray, The dimensional reduction method for identification of parameters that trade-off due to similar model roles, Mathematical Biosciences, vol.285, pp.119-127, 2017.
DOI : 10.1016/j.mbs.2017.01.003

J. Kaipio and E. Somersalo, Statistical and computational inverse problems, 2006.

A. Y. Ng, Feature selection, l 1 vs. l 2 regularization, and rotational invariance, Proceedings of the twenty-first international conference on Machine learning, p.78, 2004.

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Convex optimization with sparsity-inducing norms, Optimization for Machine Learning, vol.5, pp.19-53, 2011.
DOI : 10.1561/2200000015
URL : https://hal.archives-ouvertes.fr/hal-00937150

Y. Nesterov, A method for unconstrained convex minimization problem with the rate of convergence o (1/k2), in: Doklady an SSSR, pp.543-547, 1983.

B. O. Donoghue and E. Candes, Adaptive Restart for Accelerated Gradient Schemes, Foundations of Computational Mathematics, vol.58, issue.1, pp.715-732, 2015.
DOI : 10.1007/978-1-4419-8853-9

P. R. Johnston and R. M. Gulrajani, Selecting the corner in the L-curve approach to Tikhonov regularization, IEEE Transactions on Biomedical Engineering, vol.47, issue.9, pp.1293-1296, 2000.
DOI : 10.1109/10.867966

M. J. Powell, The bobyqa algorithm for bound constrained optimization without derivatives

M. R. Davies, H. B. Mistry, L. Hussein, C. E. Pollard, J. Valentin et al., Abi-Gerges, An in silico canine cardiac midmyocardial action potential duration model as a tool for early drug safety assessment, American Journal of Physiology-Heart and Circulatory Physiology
DOI : 10.1152/ajpheart.00808.2011
URL : http://ajpheart.physiology.org/content/ajpheart/302/7/H1466.full.pdf

Z. Syed, E. Vigmond, S. Nattel, and L. Leon, Atrial cell action potential parameter fitting using genetic algorithms, Medical & Biological Engineering & Computing, vol.4, issue.5, pp.561-571, 2005.
DOI : 10.1161/01.RES.81.5.727

A. X. Sarkar and E. A. Sobie, Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells, PLoS Computational Biology, vol.29, issue.Pt 4, p.1000914, 2010.
DOI : 10.1371/journal.pcbi.1000914.s006

J. Kaur, A. Nygren, and E. J. Vigmond, Fitting Membrane Resistance along with Action Potential Shape in Cardiac Myocytes Improves Convergence: Application of a Multi-Objective Parallel Genetic Algorithm, PLoS ONE, vol.94, issue.9, p.107984, 2014.
DOI : 10.1371/journal.pone.0107984.t004
URL : https://doi.org/10.1371/journal.pone.0107984

C. Sánchez, A. Bueno-orovio, E. Wettwer, S. Loose, J. Simon et al., Inter-Subject Variability in Human Atrial Action Potential in Sinus Rhythm versus Chronic Atrial Fibrillation, PLoS ONE, vol.279, issue.8, pp.9-105897, 2014.
DOI : 10.1371/journal.pone.0105897.s004

M. Courtemanche, R. J. Ramirez, and S. , Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model, American Journal of Physiology-Heart and Circulatory Physiology, vol.263, issue.32, pp.301-321, 1998.
DOI : 10.1016/S0006-3495(95)80271-7

J. T. Koivumäki, G. Seemann, M. M. Maleckar, and P. Tavi, In Silico Screening of the Key Cellular Remodeling Targets in Chronic Atrial Fibrillation, PLoS Computational Biology, vol.272, issue.5, p.1003620, 2014.
DOI : 10.1371/journal.pcbi.1003620.s020

L. Formaggia, A. Quarteroni, and A. Veneziani, Cardiovascular Mathematics: Modeling and simulation of the circulatory system, 2010.
DOI : 10.1007/978-88-470-1152-6

P. Reymond, F. Merenda, F. Perren, D. Rüfenacht, and N. Stergiopulos, Validation of a one-dimensional model of the systemic arterial tree, American Journal of Physiology-Heart and Circulatory Physiology, vol.297, issue.1, pp.208-222, 2009.
DOI : 10.1016/0021-9290(86)90118-1

K. S. Matthys, J. Alastruey, J. Peiró, A. W. Khir, P. Segers et al., Pulse wave propagation in a model human arterial network: Assessment of 1-D numerical simulations against in vitro measurements, Journal of Biomechanics, vol.40, issue.15, pp.40-3476, 2007.
DOI : 10.1016/j.jbiomech.2007.05.027

C. Audebert, P. Bucur, M. Bekheit, E. Vibert, I. E. Vignon-clementel et al., Kinetic scheme for arterial and venous blood flow, and application to partial hepatectomy modeling, Computer Methods in Applied Mechanics and Engineering, vol.314
DOI : 10.1016/j.cma.2016.07.009
URL : https://hal.archives-ouvertes.fr/hal-01347500