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Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, isresponsible for sudden cardiac arrests. Thus, various algorithms have beendeveloped to predict VF from Electrocardiogram (ECG), which is a binaryclassification problem. In the literature, we find a number of algorithms basedon signal processing, where, after some robust mathematical operations thedecision is given based on a predefined threshold over a single value. On theother hand, some machine learning based algorithms are also reported in theliterature; however, these algorithms merely combine some parameters and make aprediction using those as features. Both the approaches have their perks andpitfalls; thus our motivation was to coalesce them to get the best out of theboth worlds. Hence we have developed, VFPred that, in addition to employing asignal processing pipeline, namely, Empirical Mode Decomposition and DiscreteTime Fourier Transform for useful feature extraction, uses a Support VectorMachine for efficient classification. VFPred turns out to be a robust algorithmas it is able to successfully segregate the two classes with equal confidence(Sensitivity = 99.99%, Specificity = 98.40%) even from a short signal of 5seconds long, whereas existing works though requires longer signals, flourishesin one but fails in the other.

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