Time-frequency analysis based detection of ECG ST segment change using large feature set

Abstract

Early detecetion of ST segment's depression or elevation is very important for prevention of myocardial ischemia and it is very important to prevent a myocardial infarction that may occur in the future. In this study, an algorithm based on Choi-Williams time-frequency distribution was developed in order to early detection of ST segment's depressions or elevations. The performance evaluation of the algorithm was performed on a large database produced from MIT-BIH arrhythmia and European ST-T databases. From the MIT-BIH database, 111688 R-R intervals containing healthy or arrhythmias in V1, V2, V4, V5 leads and R-R intervals with 111688 ST segment's depression or elevation in V1, V2, V3, V4, V5 leads from the European ST-T database were selected. The classification performance results were found as accuracy, sensitivity, specificity and positive predictive value of 99.06%, 99.08%, 99.02% and 99.02%, respectively. These values are above the values belonging to the studies in the literature and the speed of the proposed algorithm is very suitable for telemedicine systems.