ECG beat classification with synaptic delay based artificial neural networks

In this work we present an application of Synaptic Delay Based Artificial Neural Networks to the classification of beats in ECG signal processing, both in terms of the "shape" of the P-QRS-T complex and its position in time without any explicit windowing or thresholding process. The signal is simply introduced as it is to the network, sample by sample as time passes, and the network using internal delay terms modeling the length of the synaptic connections, learns to perform all the temporal reasoning processes required for the classification through the application of Discrete Time Backpropagation. We present an example of classification using real ECG patterns from the European ST-T Database.