Combining supervised competitive learning and gradient descent learning to classify signal-averaged high-resolution electrograms

A strategy for neural network training is described. It combines supervised competitive and gradient descent learning. This algorithm is then applied to classify high-resolution electrograms. The combined approach gives an increase in classification accuracy of about 10%. Nevertheless the results show that more elaborate feature extraction methods have to be considered.