VFPred: A Fusion of Signal Processing and Machine Learning techniques in Detecting Ventricular Fibrillation from ECG Signals

Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests. Thus, various algorithms have been developed to predict VF from Electrocardiogram (ECG), which is a binary classification problem. In the literature, we find a number of algorithms based on signal processing, where, after some robust mathematical operations the decision is given based on a predefined threshold over a single value. On the other hand, some machine learning based algorithms are also reported in the literature; however, these algorithms merely combine some parameters and make a prediction using those as features. Both the approaches have their perks and pitfalls; thus our motivation was to coalesce them to get the best out of the both worlds. Sohel Rahman) Preprint submitted to Pattern Recognition July 10, 2018 a Support Vector Machine for efficient classification. VFPred turns out to be a robust algorithm as it is able to successfully segregate the two classes with equal confidence (Sensitivity 99.99%, Specificity 98.40%) even from a short signal of 5 seconds long, whereas existing works though requires longer signals, flourishes in one but fails in the other. Keywords: Electrocardiogram(ECG), Empirical Mode Decomposition, Heart Arrhythmia, Support Vector Machine, Ventricular Fibrillation(VF). 1. Introduction Ventricular Fibrillation (VF) is a type of cardiac arrhythmia which occurs when the heart quivers instead of pumping due to disturbance in electrical activity in the ventricles [1]. This arrhythmia may result in a cardiac arrest leaving the patient unconscious without any pulse. Ventricular Fibrillation is found initially in about 10% of people in cardiac arrest [2] and sudden cardiac arrest is responsible for approximately 6 million deaths in Europe and in the United States [3]. Therefore, fast and accurate detection of Ventricular Fibrillation can save a lot of lives.