Computers in biology and medicine
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Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation.
The most extended noninvasive technique for medical diagnosis and analysis of atrial fibrillation (AF) relies on the surface elctrocardiogram (ECG). In order to take optimal profit of the ECG in the study of AF, it is mandatory to separate the atrial activity (AA) from other cardioelectric signals. Traditionally, template matching and subtraction (TMS) has been the most widely used technique for single-lead ECGs, whereas multi-lead ECGs have been addressed through statistical signal processing techniques, like independent component analysis. ⋯ Regarding spectral parameters, the dominant amplitude (DA) and the mean power spectral (MP) were DA=1.15±0.18 and MP=0.31±0.07, respectively. In contrast, traditional TMS-based methods yielded, for the best case, CC=0.864±0.041, MSE=0.577±0.097, DA=0.84±0.25 and MP=0.24±0.07. The results prove that the RBF based method is able to obtain a remarkable reduction of ventricular activity and a very accurate preservation of the AA, thus providing high quality dissociation between atrial and ventricular activities in AF recordings.