Using DecisionTrees for Real-Time Ischemia Detection

Abstract

In this paper we investigate if techniques based on decision trees are also useful to classify ischemia. In order to do that, we have based on a previous own algorithm that is able of detecting ST-segment episodes, can be executed in real time and is lightweight enough to be implemented on mobile devices such as PDAs. Three main steps are performed by that algorithm: 1) signal preprocessing that extracts important features from the ECG signal 2) detection of suspect ST segment events and 3) rejection of irrelevant ST segment events. We have found that techniques based on decision trees are interesting for the last step in order to obtain a set of rules that reject some of the suspect ST segment events detected in the second step. The freely available part of the LTST database has been used to develop the detector and the rest of the same database has been used for validation purposes. The sensitivity of the detector over all the records of the LTST database is 89.89% for episodes annotated according to C protocol and the positive predictivity is 70.03% for episodes annotated according to A protocol. Those results improve our previously obtained ones and we think that they are comparable to other results that appear in the literature. However, it has to be noticed that we have not found works that detect ischemia in real time and show validation results over the LTST database