Detection of Ventricular Arrhythmia from ECG

Abstract

The Ventricular Arrhythmias particularly Ventricular Tachycardia (VT) and Ventricular Flutter (VF) are life threatening arrhythmias and can lead to heart attacks if not detected and treated timely. In this paper, a method has been proposed that can differentiate between normal Electrocardiograms (ECGs) and two abnormal ECGs of VT and VF. The classification is performed by means of Artificial Neural Networks (ANN). Reflection coefficients of the Auto-Regressive Models of extractions from the ECG recordings are computed and used as features for input to the ANN. The ANN is trained using ECG samples that are characteristic of Non-diseased (normal), VT and VF. After suitable training and validation, the proposed algorithm has been found to have an accuracy of 100%, 97% and 94% for classification of Normal ECG, VF and VT respectively.