Classifying ischemic events using a Bayesian inference Multilayer Perceptron and input variable evaluation using automatic relevance determination

Abstract

In this paper we present a Bayesian inference Multilayer Perceptron (MLP) which was used to classify the events of the Long Term ST Database (LTSTDB) as ischaemic or non-ischaemic episodes with an accuracy of 89.1%, sensitivity of 82.3% and specificity of 91.2% when the accuracy of the winning paper was 90.7%. The Automatic Relevance Determination (ARD) method was used to identify which of the extracted features that were used as input in the Bayesian inference MLP were the most important with respect to the models performance. ARD indicated that DeltaT, a combination of the ST deviation and the duration of the episode, inspired from Langley et al., was the most important feature for determining Ischaemic episodes, given the data. A simple MLP which had as input variable of only DeltaT was trained to verify the results of the ARD method. The classification accuracy was 85.8% on the test set. We can conclude from the results that the most important extracted feature was DeltaT.