Asie Bakhshipour - Biomedical Engineering Department Shahed University Tehran, Iran
Mohammad Pooyan - Biomedical Engineering Department Shahed University Tehran, Iran
Hajat Mohammadnejad - Biomedical Engineering Department Shahed University Tehran, Iran
Alireza Fallahi - Electrical Engineering Department Hamedan University of Technology Hamedan, Iran

In this paper, we propose a novel method for the detection of myocardial ischemic events from electrocardiogram (ECG) signal, using the Discrete Wavelet Transform (DWT) technique and Support Vector Machines (SVM). The ST-T Segment is obtained based on the detection of R peak location based on the well-known Pan-Tompkins method. Then ratio of energy in the DWT approximation coefficients rather than detail coefficients calculated as the features. SVM is used to build classifiers for ischemic and normal ECG signals. The proposed method achieved correct rate of 98.2%, sensitivity of 98.43% and specificity of 99.45%.

ECG; Myocardial Ischemia; Wavelet transform; SVM