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

被引：12

作者：

Mailhe, B. ^{[1
]}

Gribonval, R. ^{[1
]}

Bimbot, F. ^{[1
]}

Lemay, M. ^{[2
]}

Vandergheynst, P. ^{[2
]}

Vesin, J. -M. ^{[2
]}

机构：

[1] IRISA, INRIA, Projet METISS, Campus Beaulieu, F-35042 Rennes, France

[2] Ecole Polytech Fed Lausanne, Signal Proc Lab Sch Engn, CH-1015 Lausanne, Switzerland

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年

关键词：

ECG; atrial fibrillation; monochannel source separation; dictionary learning; sparse approximation; K-SVD;

D O I：

10.1109/ICASSP.2009.4959621

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：465 / +

页数：2

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