Deciding optimal number of exemplars for designing an ECG pattern classifier using MLP
R. B. Ghongade · Indian Journal of Science and Technology · 2009
ECG pattern recognition using artificial neural networks is now an established paradigm. Diagnostic systems derive robustness, reliability and speed because of the automatic pattern classifiers. However, a common problem associated with these types of classifiers is to decide the optimal number of exemplars. This paper attempts to find an optimal number of exemplars required for training a multilayer perceptron with acceptable accuracy. Extensive experimentation suggests a figure of 200. Although this figure is specific for multilayer perceptron based classifier, experimentation on similar lines can be performed for other ANN topologies. Keywords: ECG, MLP, pattern classifier, optimal number of exemplars