Myocardial ischemia detection with ECG analysis, using Wavelet Transform and Support Vector Machines

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%.