Early prediction of ventricular tachyarrhythmias based on heart rate variability analysis

Ventricular tachyarrhythmias (VTAs) are fatal events and it is obvious that early prediction of VTAs could help in reducing mortality rate due to sudden cardiac death (SCD). Heart rate variability (HRV) reflects all symptoms associated with autonomic nervous system (ANS) as well as heart disease. Thus, HRV has frequently been used in various studies. We collected 220 recordings (VTAs - ventricular tachycardia (VT) and ventricular fibrillation(VF): 110, Control data: 110) from 81 adult patients in Intensive care unit (lCU), Asan Medical Centar (AMC) and proposed three classifiers for prediction of VT As events using eleven HRV parameters. Our group already developed a predictor for VTAs using ventricular arrhythmias dataset in Physionet before 10 seconds ahead of the events. In this study, we tried to predict VTAs earlier than an hour using parameters from HRV analysis and artificial neural network (ANN) models. The ANN model for prediction of VTAs showed a significantly high accuracy as 86.11 % (189/220) and Area under the curve (AVC) of receiver operating characteristic (ROC) was 0.88.