ST Segment Change Detection by Means of Wavelets

Abstract

This research aims to contribute to the automatic interpretation of long sequences of electrocardiograms (ECG) typical for Holter monitoring. We developed a method that uses wavelets for extracting ECG patterns that are characteristic for myocardial ischemia. It was our intention to detect the beats in the simplest possible manner and generate a quantitative estimate of myocardial ischemia likelihood which would suit needs of cardiologists. Biorthogonal wavelets were applied in order to define ST segment properties at different scales. The new method was tested on data from the European ST-T change database. Results show that this method it effective for distinguishing normal from ischemic ECGs. The element that makes the distinction is the correlation of number of ST deviations with the time of consecutive appearances