S. Ghosh and R. Y. , Application of L1-Norm Regularization to Epicardial Potential Solution of the Inverse Electrocardiography Problem, Annals of Biomedical Engineering, vol.289, issue.5, p.902912, 2009.
DOI : 10.1007/s10439-009-9665-6

B. Scholkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond . Adaptive Computation and Machine Learning, 2001.

J. Sundnes, G. Lines, X. Cai, B. Nielsen, K. Mardal et al., Computing the electrical activity in the heart, 2006.

M. Boulakia, S. Cazeau, M. Fernández, J. Gerbeau, and N. Zemzemi, Mathematical Modeling of Electrocardiograms: A Numerical Study, Annals of Biomedical Engineering, vol.98, issue.1???3, pp.1071-1097, 2010.
DOI : 10.1007/s10439-009-9873-0

URL : https://hal.archives-ouvertes.fr/inria-00400490

G. Beeler and H. Reuter, Reconstruction of the action potential of ventricular myocardial fibres, The Journal of Physiology, vol.268, issue.1, pp.177-210, 1977.
DOI : 10.1113/jphysiol.1977.sp011853

R. Klepfer, C. Johnson, and R. Macleod, The effects of inhomogeneities and anisotropies on electrocardiographic fields:a three-dimensional finite element study, IEEE Eh4BC and CMBEC, 1995.

C. Saunders, A. Gammerman, and V. Vovk, Ridge regression learning algorithm in dual variables, In ICML, pp.515-521, 1998.