Atrial activity enhancement by blind sparse sequential separation

Blind source separation (BSS) has been probed as one of the most effective techniques for the atrial activity (AA) extraction in supraventricular tachyarrhythmia episodes like atrial fibrillation (AF). In these situations, the registered episodes with only a few leads are noisy and time-varying and previous stages for sparse separation have been demonstrated as necessary. Including wavelet transform de-noising and natural gradient algorithm for the BSS system can improve the extraction quality with low computational load. Synthetic signals have been used to test the proposed technique in different noisy cases. The obtained cross-correlation coefficients with sparse sequential separation between the extracted signal and the original ones exceed the 94% in contrast with the obtained results using standalone BSS method. The easy and fast implementation and the minimum required reference recordings of the same ECG are some of the main advantages of this technique and make the application in real time systems possible