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. In this contribution, a new QRST cancellation method based on a radial basis function (RBF) neural network is proposed. The system is able to provide efficient QRST cancellation and can be applied both to single and multi-lead ECG recordings. The learning algorithm used for training the RBF makes use of a special class of network, known as cosine RBF, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of the network. The experiments verify that RBFs trained by the proposed learning algorithm are capable of reducing the QRST complex dramatically, a property that is not shared by other methods and conventional feed-forward neural networks. Average Results (mean   std) for the RBF method in cross-correlation (CC) between original and estimated AA are CC=0.950.038 CC = 0.95  0.038 being the mean square error (MSE) for the same signals, MSE=0.3110.078 MSE = 0.311  0.078 . Regarding spectral parameters, the dominant amplitude (DA) and the mean power spectral (MP) were DA=1.150.18 DA = 1.15  0.18 and MP=0.310.07 MP = 0.31  0.07 , respectively. In contrast, traditional TMS-based methods yielded, for the best case, CC=0.8640.041 CC = 0.864  0.041 , MSE=0.5770.097 MSE = 0.577  0.097 , DA=0.840.25 DA = 0.84  0.25 and MP=0.240.07 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.
radial basis function neural networks qrst cancellation atrial fibrillation ecg electrocardiogram