Atrial activity enhancement by Wiener filtering using an artificial neuralnetwork

This paper describes a novel technique for the cancellation of the ventricu lar activity for applications such as P-wave or atrial fibrillation detecti on. The procedure was thoroughly tested and compared with a previously publ ished method, using quantitative measures of performance. The novel approac h estimates, by means of a dynamic time delay neural network (TDNN), a time -varying, nonlinear transfer function between two ECG leads. Best results w ere obtained using an Elman TDNN with nine input samples and 20 neurons, em ploying a sigmoidal tangencial activation in the hidden layer and one linea r neuron in the output stage. The method does not require a previous stage of QRS detection. The technique was quantitatively evaluated using the MIT- BIH arrhythmia database and compared with an adaptive cancellation scheme p roposed in the literature. Results show the advantages of the proposed appr oach, and its robustness during noisy episodes and QRS morphology variation s.