VFPred: A fusion of signal processing and machine learning techniques in detecting ventricular fibrillation from ECG signals. (March 2019) - Ibtehaz, Nabil; Rahman, M. Saifur; Rahman, M. Sohel
Highlights: We present an elegant feature engineering scheme by exploiting the characteristics of a ventricular fibrillation class ECG signal. Subsequently, we apply machine learning techniques and develop and robust predictor called VFPred. VFPred thus is a fusion of both signal processing and machine learning techniques. We have conducted extensive experiments and according to the experimental results, VFPred outperforms the state of the art. Abstract: 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. Hence we have developed, VFPred that, in addition to employing a signal processing pipeline, namely, Empirical Mode Decomposition and Discrete Fourier Transform for useful feature extraction, uses 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 s long, whereas existing works though requires longer signals, flourishes in one but fails in the other.