DICTIONARY LEARNING FOR THE SPARSE MODELLING OF ATRIAL FIBRILLATION IN ECG SIGNALS

Abstract : We propose a new method for ventricular cancellation and atrial modelling in the ECG of patients suffering from atrial fibrillation. Our method is based on dictionary learning. It extends both the average beat subtraction and the sparse source separation approaches. Experiments on synthetic data show that this method can almost completely suppress the ventricular activity, but it generates some artifacts. Contrary to other ventricular cancellations methods, our approach also learns a model for the atrial activity.
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Contributor : Boris Mailhé <>
Submitted on : Thursday, March 25, 2010 - 2:48:03 PM
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Boris Mailhé, Rémi Gribonval, Frédéric Bimbot, Mathieu Lemay, Pierre Vandergheynst, et al.. DICTIONARY LEARNING FOR THE SPARSE MODELLING OF ATRIAL FIBRILLATION IN ECG SIGNALS. ICASSP 2009, Apr 2009, Taipei, Taiwan. pp.465 - 468, ⟨10.1109/ICASSP.2009.4959621⟩. ⟨hal-00466973⟩

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