Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

ABSTRACT: BACKGROUND: Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome inelectrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophicdisease. To this end, we propose a new method, which employs wavelets and simple feature selection. METHODS: For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method basedon the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used fordifferentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalizedand signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point.We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiersto those features. RESULTS: We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods.Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemicST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively.The SVM classifier detects 355 ischemic ST episodes. CONCLUSIONS: We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removingbaseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and featureextraction from morphology of ECG waveforms explicitly. It was shown that the number of selected featureswere sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposedKDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require anynumerical values of the parameters to be supplied in advance. In the case of the SVM classifier, one has to selecta single parameter.