Optimisation of neural network training through pre-establishment of synaptic weights applied to body surface mapping classification

Abstract

In this paper, we present a modified perceptron that has been optimised to enhance its generalization capabilities through pre-initialisation of the synaptic weights. The rationale for the research is presented highlighting the obvious benefits of such an approach. A description of results obtained from an experiment seeking cardiac classification is presented. The dataset used contained 74 patient records; 30 inferior myocardial infarction and 44 normal. Patient records where acquired using 192-lead body surface potential maps (BSPM). QRS isointegral maps where derived from the 192 lead maps before applying principal component analysis to reduce the dimensionality of the dataset. Only the first 2 principal components were used to create a 2 dimensional space, which can be both easily visualized and modeled by a single perceptron. In addition to the optimised approach proposed, several other classification methods were evaluated on the dataset to generate benchmarks from which comparisons were made. These included linear, probabilistic, non-linear and neural network methods. The optimised approach resulted in sensitivity and specificity figures of 73.33% and 70.45% respectively and provided an overall accuracy which was 2.5% higher than that of the next best classifier.