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Conference Papers Year : 2012

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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Dates and versions

hal-00759210 , version 1 (30-11-2012)

Identifiers

  • HAL Id : hal-00759210 , version 1

Cite

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. pp.125-128. ⟨hal-00759210⟩
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