Abstract
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.
Highlights
Ventricular fibrillation (VF) is one of the most common cardiac arrhythmias in patients with sudden cardiac death (SCD) [1]
We developed a new deep learning method based on the convolutional neural network (CNN) to effectively predict the development of VF before its onset
The proposed approach achieved an accuracy of 99.036%, 99.800%, and 81.250% for the classification of shockable versus nonshockable, VF versus non-VF, and ventricular tachycardia (VT) versus VF, respectively
Summary
Ventricular fibrillation (VF) is one of the most common cardiac arrhythmias in patients with sudden cardiac death (SCD) [1]. In myocardial ischemia-related VF, clinicians have noticed that VF may be preceded by unique electrocardiographic (ECG) findings, such as multiple premature ventricular contractions (PVCs), ST segment changes, R on T phenomenon, pauses, QT prolongation, VT, supraventricular arrhythmias, and even the very common sinus tachycardia. None of these findings is specific enough to predict the development of VF. Conventional machine learning methods, such as support vector machine (SVM) [2] or multilayer perceptron (MLP) neural network [3], have been used to predict life-threatening cardiac arrhythmias These methods utilize certain explicitly recognizable features (such as the RR interval) [2] derived from standard clinical ECG analysis. According to the desired alert time before VF onset, we achieved 99% recall and 97% accuracy to predict the development of VF
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