Development of SVM based ECG Pattern Recognition Technique

Abstract

This paper presents simple and reliable method for the detection of QRS complexes (cardiac beat) in single lead Electrocardiograms (ECG) using Support Vector Machine (SVM). Two different preprocessing techniques are applied for the generation of features. First involves digital filtering to remove base line wander and power line interference while the second involves entropy criterion for feature generation. SVM is used as a classifier to classify QRS and non-QRS regions. The results of the validation of the method on the standard CSE ECG database are also presented. Detection rate of 99.79 % is achieved. The percentage of false positive and false negative is 0.86% and 0.21% respectively. The proposed method functions reliably even under the condition of poor signal quality of the ECG. The detection rate depends strongly on the quality of training, data representation and the mathematical basis of the classifier.