From Body Surface Potential to Activation Maps on the Atria: a Machine Learning Technique

Abstract : The treatment of atrial fibrillation has greatly changed in the past decade. Ablation therapy, in particular pul- monary vein ablation, has quickly evolved. However, the sites of the trigger remain very difficult to localize. In this study we propose a machine-learning method able to non-invasively estimate a single site trigger. The machine learning technique is based on a kernel ridge regression al- gorithm. In this study the method is tested on a simulated data. We use the monodomain model in order to simulate the electrical activation in the atria. The ECGs are com- puted on the body surface by solving the Laplace equation in the torso.
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Communication dans un congrès
CINC - Computing in Cardiology 2012, Sep 2012, Krakow, Poland. 39, pp.125-128, 2012, Computing in Cardiology 2012,. 〈www.cinc.org/archives/2012/pdf/0125.pdf〉
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Nejib Zemzemi, Simon Labarthe, Rémi Dubois, Yves Coudière. From Body Surface Potential to Activation Maps on the Atria: a Machine Learning Technique. CINC - Computing in Cardiology 2012, Sep 2012, Krakow, Poland. 39, pp.125-128, 2012, Computing in Cardiology 2012,. 〈www.cinc.org/archives/2012/pdf/0125.pdf〉. 〈hal-00759210〉

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