M. Alawad and L. Wang, Learning domain shift in simulated and clinical data: Localizing the origin of ventricular activation from 12-lead electrocardiograms, IEEE transactions on medical imaging, 2018.

S. Giffard-roisin, H. Delingette, T. Jackson, J. Webb, L. Fovargue et al., Transfer learning from simulations on a reference anatomy for ecgi in personalized cardiac resynchronization therapy, IEEE Transactions on Biomedical Engineering, vol.66, issue.2, pp.343-353, 2019.

A. Karoui, L. Bear, P. Migerditichan, and N. Zemzemi, Evaluation of fifteen algorithms for the resolution of the electrocardiography imaging inverse problem using ex-vivo and in-silico data, Frontiers in Physiology, vol.9, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01923763

C. T. Lin, H. W. Nein, and W. C. Lin, A space-time delay neural network for motion recognition and its application to lipreading, International Journal of Neural Systems, vol.9, issue.04, pp.311-334, 1999.

A. Malik, T. Peng, and M. L. Trew, A machine learning approach to reconstruction of heart surface potentials from body surface potentials, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.4828-4831, 2018.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic differentiation in pytorch, 2017.

J. Wang, I. Tsapakis, and C. Zhong, A space-time delay neural network model for travel time prediction, Engineering Applications of Artificial Intelligence, vol.52, pp.145-160, 2016.
DOI : 10.1016/j.engappai.2016.02.012

N. Zemzemi, R. Dubois, Y. Coudiere, O. Bernus, and M. Haissaguerre, A machine learning regularization of the inverse problem in electrocardiography imaging, Computing in Cardiology, pp.1135-1138, 2013.

N. Zemzemi, S. Labarthe, R. D. Dubois, and Y. Coudière, From body surface potential to activation maps on the atria: A machine learning technique, Computing in Cardiology, pp.125-128, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00759210