Comput. Biol. Med. - Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation.

Tópicos

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Resumo

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=0.95?0.038 being the mean square error (MSE) for the same signals, MSE=0.311?0.078. 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.

Resumo Limpo

extend noninvas techniqu medic diagnosi analysi atrial fibril af reli surfac elctrocardiogram ecg order take optim profit ecg studi af mandatori separ atrial activ aa cardioelectr signal tradit templat match subtract tms wide use techniqu singlelead ecg wherea multilead ecg address statist signal process techniqu like independ compon analysi contribut new qrst cancel method base radial basi function rbf neural network propos system abl provid effici qrst cancel can appli singl multilead ecg record learn algorithm use train rbf make use special class network known cosin rbf updat select adjust paramet minim classcondit varianc output network experi verifi rbfs train propos learn algorithm capabl reduc qrst complex dramat properti share method convent feedforward neural network averag result mean std rbf method crosscorrel cc origin estim aa cc mean squar error mse signal mse regard spectral paramet domin amplitud da mean power spectral mp da mp respect contrast tradit tmsbase method yield best case cc mse da mp result prove rbf base method abl obtain remark reduct ventricular activ accur preserv aa thus provid high qualiti dissoci atrial ventricular activ af record

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