On improving the classification of myocardial ischemia using Holter ECG data

In this paper, a method is proposed to improve an algorithm for myocardial ischemia classification created by Langley et al. The Langley classifier achieves a very high sensitivity (99.0%), but a lower specificity value (93.3%). In order to improve the specificity, the proposed algorithm attempts to reclassify the events that the Langley classifier labels ischemic. The classifier used is a support vector machine. The features used are the mean of the ST deviation, maximum value of the ST deviation, and the initial ST deviation. The classifier is able to increase the specificity from 92.3% to 93.3%. The drawback is that the sensitivity is reduced from 99.0% to 97.5%. This causes the overall accuracy to decrease slightly from 95.6 to 94.8. The algorithm shows promise in being able to increase specificity, but work must be done to find features that do not cause such a large decrease in the sensitivity.