Support vector machines for detection of electrocardiographic changes in partial epileptic patients

The aim of this study is to evaluate the diagnostic accuracy of the support vector machines (SVMs) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. Post-ictal heart rate oscillations were studied in a heterogeneous group of patients with partial epilepsy. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem, and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features, which well represent the ECG signals, and the SVMs trained on these features achieved high classification accuracies (total classification accuracy was 99.44%).