NDLI: From body surface potential to activation maps on the atria: A machine learning technique

Content Provider	IEEE Xplore Digital Library
Author	Zemzemi, N. Labarthe, S. Dubois, R. Coudiere, Y.
Copyright Year	2012
Description	Author affiliation: LIRYC Université de Bordeaux, Pessac, France (Dubois, R.) \|\| INRIA Bordeaux Sud-Ouest, Talence, France (Zemzemi, N.; Labarthe, S.; Coudiere, Y.)
Abstract	The treatment of atrial fibrillation has greatly changed in the past decade. Ablation therapy, in particular pulmonary 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 algorithm. 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 computed on the body surface by solving the Laplace equation in the torso.
Starting Page	125
Ending Page	128
File Size	421204
Page Count	4
File Format	PDF
ISBN	9781467320764
ISSN	2325887X
e-ISBN	9781467320771
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-09
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Creative Commons Attribution License 2.5 (CCAL)
Subject Keyword	Mathematical model Electric potential Heart Torso Training Kernel Training data
Content Type	Text
Resource Type	Article
Subject	Computer Science Cardiology and Cardiovascular Medicine

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From body surface potential to activation maps on the atria: A machine learning technique