Predicting Ventricular Fibrillation Through Deep Learning

Ventricular fibrillation (VF) is a type of cardiac arrhythmia. This chaotic cardiac electrical activity results in heart quivering instead of normal pumping. To date, early cardiopulmonary resuscitation (CPR) and defibrillation are the only effective VF treatment. Acute myocardial infarction is the most common cause of VF, and cardiomyopathy, myocarditis, electrolyte imbalance, cardiotoxic medication, and even ion channel abnormality can cause VF. Physicians have attempted to identify specific patterns in electrocardiography (ECG) that might predict VF in the short term. For example, ST segment changes might imply coronary artery occlusion with myocardial ischemia, increasing VF risk. However, in most cases, VF occurs abruptly without any early warning. Machine learning is used to extract information usually neglected by the human brain. In deep learning, a cascade of multiple layers of processing is used to extract features. Machine learning is used to classify different types and outcomes of cardiac arrhythmias that are difficult to recognize directly. In this study, we developed a new deep learning method to predict the onset of VF. ECG from MIT-BIH databases were used as the training and validation data sets; the prediction results showed that the proposed two-dimensional short-time Fourier transform (2D STFT)/continuous wavelet transform (CWT) convolutional neural network (CNN) model can reach a recall of 99% and an accuracy of 97%. We also compared the proposed 2D model with 1D and 2D time-domain CNN models. The results showed that the 1D CNN and 2D time-domain models can achieve an accuracy of 60.5% and 56%, respectively.