B. J. Messinger-rapport and R. Y. , Computational issues of importance to the inverse recovery of epicardial potentials in a realistic heart-torso geometry, Mathematical Biosciences, vol.97, issue.1, pp.85-120, 1989.

B. J. Messinger-rapport and R. Y. , Regularization of the inverse problem in electrocardiography: A model study, Mathematical Biosciences, vol.89, issue.1, pp.79-118, 1988.

C. Figuera, V. Suárez-gutiérrez, I. Hernández-romero, R. M. Liberos, A. Atienza et al., Alonso-Atienza F. Regularization techniques for ecg imaging during atrial fibrillation: a computational study, Frontiers in physiology, vol.7, p.466, 2016.

L. K. Cheng, J. M. Bodley, and A. J. Pullan, Comparison of potentialand activation-based formulations for the inverse problem of electrocardiology, IEEE Transactions on Biomedical Engineering, vol.50, issue.1, pp.11-22, 2003.

J. P. Barnes and P. R. Johnston, Application of robust generalised cross-validation to the inverse problem of electrocardiology, Computers in biology and medicine, vol.69, pp.213-225, 2016.

F. Bauer and M. A. Lukas, Comparingparameter choice methods for regularization of ill-posed problems, Mathematics and Computers in Simulation, vol.81, issue.9, pp.1795-1841, 2011.
DOI : 10.1016/j.matcom.2011.01.016

URL : http://researchrepository.murdoch.edu.au/id/eprint/4407/1/parameter_choice_methods.pdf

P. C. Hansen, Rank-Deficient and Discrete Ill-posed Problems, 1998.
DOI : 10.1137/1.9780898719697

P. Johnston and R. Gulrajani, A new method for regularization parameter determination in the inverse problem of electrocardiography, IEEE Transactions on Biomedical Engineering, vol.44, issue.1, pp.19-39, 1997.

D. Krawczyk-stado and M. Rudnicki, Regularization parameter selection in discrete ill-posed problems-the use of the ucurve, International Journal of Applied Mathematics and Computer Science, vol.17, issue.2, pp.157-164, 2007.

P. C. Hansen, Truncated singular value decomposition solutions to discrete ill-posed problems with ill-determined numerical rank, SIAM Journal on Scientific and Statistical Computing, vol.11, issue.3, pp.503-518, 1990.
DOI : 10.1137/0911028

J. Chamorro-servent, R. Dubois, M. Potse, C. , and Y. , Improving the spatial solution of electrocardiographic imaging: A new regularization parameter choice technique for the tikhonov method, Functional Imaging and Modelling of the Heart, pp.289-300, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01564899

P. Colli-franzone, L. Guerri, S. Tentoni, C. Viganotti, S. Baruffi et al., A mathematical procedure for solving the inverse potential problem of electrocardiography. analysis of the time-space accuracy from in vitro experimental data, Mathematical Biosciences, vol.77, issue.1, pp.353-396, 1985.

G. Wahba, Practical approximate solutions to linear operator equations when the data are noisy, SIAM Journal on Numerical Analysis, vol.14, issue.4, pp.651-667, 1977.
DOI : 10.1137/0714044

M. A. Lukas, Robust generalized cross-validation for choosing the regularization parameter, Inverse Problems, vol.22, issue.5, p.1883, 2006.
DOI : 10.1088/0266-5611/22/5/021

URL : http://researchrepository.murdoch.edu.au/id/eprint/11545/1/robust_generalized_cross-validation.pdf

L. R. Bear, P. R. Huntjens, R. Walton, O. Bernus, R. Coronel et al., Cardiac electrical dyssynchrony is accurately detected by noninvasive electrocardiographic imaging, Heart Rhythm, 2018.
DOI : 10.1016/j.hrthm.2018.02.024

URL : https://doi.org/10.1016/j.hrthm.2018.02.024