Detection of myocardial ischemia episode using morphological features

In this study, we propose to use morphological features that are easy to identify to differentiate myocardial ischemic beats from normal beats. In general, myocardial ischemia causes alterations in electrocardiographic (ECG) signal such as deviation in the ST segment. When the ST segment level deviates from a certain voltage, the beat would be diagnosing as myocardial ischemia. To emphasize on ST variations, the QRS complex of the ECG signal was first subtracted and replaced with a straight line. Five-level discrete wavelet transform (DWT) followed to decompose the waveform into subband components and the A5 subband, which is most sensitive to the changes in the ST segment, was reconstructed for the calculation of 12 morphological features. The support vector machine (SVM) and the 10-fold cross-validation method were employed to evaluate the performance of the method. The results show high values of 95.20%, 93.29%, and, 93.63% in sensitivity, specificity, and accuracy, respectively, that were demonstrated to outperform the other methods in the literature.