Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals

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.


Published in:
Proc. EEE International Conference on Acoustics, Speech and Signal Processing (ICASSP09)
Presented at:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP09), Taipeh, 2009
Year:
2009
Publisher:
Taipei, Taiwan
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 Record created 2009-01-20, last modified 2018-01-28

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