Paper Title
Classification Of ECG Beats Using Theteager Energy Operator And Support Vector Machines

Abstract
In order to classify individual beats in ECG signals, it is first necessary to extract features from the signal. This process may require high complexity tools, which might be time consuming. In light of this, we propose in this paper the use of the Teager Energy Operator (TEO) to detect the QRS complexes of the ECG signal, due to the fact that this tool isconsiderably simpler when compared to other tools. We performed an evaluation of its feature extraction efficiency by using Support Vector Machines (SVM) to classify ECG beats of which the features had been extracted by the TEO. The classification results showed an overall accuracy of 99.18%, showing that the TEO can be used as a viable and simple tool to aid in the classification process of ECG signals. Index Terms: Classification, ECG, Teager Energy Operator, Support Vector Machines.