A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems

In this paper, we introduce a novel system for ECG beat recognition using Support Vector Machine (SVM) classifier designed by a perturbation method. Three feature extraction methods are comparatively examined in reduced dimensional feature space. The dimension of each feature set is reduced by using perturbation method. If there exist redundant data components in training data set, they can be discarded by analyzing the total disturbance of the SVM output corresponding to the perturbed inputs. Thus, the input dimension size is reduced and network becomes smaller. Algorithm for input dimension reduction is first formulated and then applied to real ECG data for recognition of beat patterns. After the preprocessing of ECG data, four types of ECG beats obtained from the MIT-BIH database are recognized with the accuracy of 96.5% by the proposed system together with discrete cosine transform. (c) 2005 Elsevier Ltd. All rights reserved.