Ventricular Fibrillation Detection: Harnessing ECG Metrics for Accurate Classification

Abstract

Ventricular Fibrillation (VF) is a life-threatening cardiac arrhythmia that demands prompt and accuratedetection to prevent sudden cardiac arrest and increase survival rates. Electrocardiogram (ECG) signalsare a primary diagnostic tool in identifying such arrhythmic patterns, and leveraging machine learning techniques can significantly enhance the accuracy and efficiency of VF detection