Abstract
Detection and delineation of QRS-complexes, P and T-waves, are important issues in the analysis and interpretation of Electrocardiogram (ECG) signals. In this paper, a classifier motivated from statistical learning theory, i.e., Support Vector Machine (SVM), has been explored for detection and delineation of these wave components. Digital filtering techniques are used to remove interference present in ECG signal. The feature extraction is done using a modified definition of slope of the ECG signals. The performance of the proposed algorithm is validated using ECG recordings from dataset-3 of the CSE multi-lead measurement library. The results in terms of accuracy, i.e., 94.4%, obtained clearly indicate a high degree of agreement with the manual annotations made by the referees of CSE dataset-3.
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