Paper Info
Title
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.
Year
DOI
Venue
2009
10.1109/ICASSP.2009.4959621
ICASSP
Keywords
Field
DocType
ecg signal,average beat subtraction,index termsó ecg,dictionary learning,atrial modelling,sparse modelling,sparse approximation,ventricular cancellation,ventricular cancellations method,sparse source separation approach,monochannel source separation,ventricular activity,atrial brillation,atrial fibrillation,k-svd,new method,atrial activity,independent component analysis,signal processing,k svd,probability density function,synthetic data,indexation,lead,dictionaries,data mining,hemodynamics
Atrial fibrillation,Pattern recognition,K-SVD,Computer science,Sparse approximation,Speech recognition,Synthetic data,Artificial intelligence,Independent component analysis,Electrocardiography,Subtraction,Source separation
Conference
ISSN
Citations 
PageRank 
1520-6149
7
0.50
References 
Authors
4
6
Name
Order
Citations
PageRank
B. Mailhe170.50
R. Gribonval22347282.40
F. Bimbot327025.28
Mathieu Lemay418316.13
pierre vandergheynst551058.37
J.-M. Vesin612112.33