Prediction of Ventricular Tachyarrhythmia in Electrocardiograph Signals Using Neural Network and Modified Nearest Neighbour Method

Ventricular Tachyarrhythmias, especially Ventricular Fibrillation, are the primary arrhythmias which are cause of sudden death. The object of this study is to characterize Ventricular Fibrillation prior to its onset because only care is in-time defibrillation. Two prediction methodologies are being presented here i.e. Neural Network and Nearest Neighbour. Standard Nearest Neighbour method is modified and evaluated for Prediction. Electrocardiograph signals of patients are studied having three types of arrhythmia i.e. Ventricular Tachycardia, Ventricular Flutter and Ventricular Fibrillation. Electrocardiographs of subjects having normal sinus rhythm are also studied. For Classification of theses signals, Generalized Regression Neural Network, standard and modified Nearest Neighbour methods used. VF is often proceeds by episodes of VT. These methods can recognize VT class so onset of VF can be predicted before time. Promising results are found.