Indian Journal of Science and Technology
Year: 2009, Volume: 2, Issue: 4, Pages: 40-42
R. B. Ghongade1 and A. A. Ghatol2
1Vishwakarma Instt. of Information Technology, Pune;
2Dr. Babasaheb Ambedkar Technological Univ , Lonere, India
*Author for the correspondence:
R. B. Ghongade
Vishwakarma Instt. of Information Technology, Pune
E-mail: [email protected]
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
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