A Machine Learning Technique Regularization of the Inverse Problem in Cardiac Electrophysiology

Nejib Zemzemi (1) , Rémi Dubois (2) , Yves Coudière (1) , Olivier Bernus (3) , M. Haïssaguerre (4)

1 CARMEN - Modélisation et calculs pour l'électrophysiologie cardiaque
2 IHU-LIRYC
3 Université de Bordeaux Ségalen [Bordeaux 2]
4 CRCTB - Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux]

Radio-frequency ablation is one of the most efficient treatments of atrial fibrillation. The idea behind it is to stop the propagation of ectopic beats coming from the pulmonary vein and the abnormal conduction pathways. Medical doctors need to use invasive catheters to localize the position of the triggers and they have to decide where to ablate during the intervention. ElectroCardioGraphy Imaging (ECGI) provides the opportunity to reconstruct the electrical potential and activation maps on the heart surface and analyze data prior to the intervention. The mathematical problem behind the reconstruction of heart potential is known to be ill posed. In this study we pro- pose to regularize the inverse problem with a statistically reconstructed heart potential, and we test the method on synthetically data produced using an ECG simulator.

Domaines: Sciences du Vivant [q-bio]; Sciences de l'ingénieur [physics]; Mathématiques générales [math.GM]

Nejib Zemzemi, Rémi Dubois, Yves Coudière, Olivier Bernus, M. Haïssaguerre. A Machine Learning Technique Regularization of the Inverse Problem in Cardiac Electrophysiology. CinC - Computing in Cardiology Conference, Sep 2013, Zaragoza, Spain. pp.285-312. ⟨hal-00920954⟩