Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features

Abstract

predicting the occurrence of ventricular tachyarrhythmia (VtA) in advance is a matter of utmost importance for saving the lives of cardiac arrhythmia patients. Machine learning algorithms have been used to predict the occurrence of imminent VtA. in this study, we used a one-dimensional convolutional neural network (1-D CNN) to extract features from heart rate variability (HRV), thereby to predict the onset of VtA. We also compared the prediction performance of our cnn with other machine leaning (ML) algorithms such as an artificial neural network (ANN), a support vector machine (SVM), and a k-nearest neighbor (KNN), which used 11 HRV features extracted using traditional methods. The proposed CNN achieved relatively higher prediction accuracy of 84.6%, while the ANN, SVM, and KNN algorithms obtained prediction accuracies of 73.5%, 67.9%, and 65.9% using 11 HRV features, respectively. Our result showed that the proposed 1-D CNN could improve VTA prediction accuracy by integrating the data cleaning, preprocessing, feature extraction, and prediction. Heartbeat is regulated by electrical signals conducted across the four chambers of the heart: two atria and two ventricles. When electrical activity is normal, the heart beats approximately 60 to 100 times per minute. However, abnormal electrical signals in the heart lead to disorganized electrical activities such as ventricular tachyarrhythmia (VTA), which causes fast heart rate 1. Thus, early VTA prediction helps physicians to take immediate medical procedure to reduce the risk. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the most common VTAs. VT arises from improper electrical activity in the ventricles, and can cause sudden cardiac arrest. VF is caused by chaotic electrical activity in the ventricles, which is similar to VT, but is a fatal condition that requires immediate medical attention. In VF, the heart shivers instead of pumping blood. Developing earlier preventive interventions would reduce the risk of experiencing an imminent VT and VF events. Researchers used noninvasive tests by measuring and analyzing electrocardiograms (ECGs), where heart rate variability (HRV) is extracted to train machine learning (ML) algorithms for predicting VT or VF in advance 2. HRV is the most commonly employed biomarker for isolating VT or VF subject from the normal subject 3. It is a time variation of heartbeats among two successive QRS complexes (Q, R, and S waves in ECG). In recent years, HRV indices have been used as a noninvasive biomarkers to forecast life-threatening arrhythmias 4. Previous studies mainly used the three traditional analysis methods: time domain, frequency domain, and Poincare nonlinear analyses, to extract features from HRV. Furthermore, they used these features as input to machine learning algorithms to predict the occurrence of VT, VF, or both. The machine learning techniques are used to classify the complex feature patterns and enable early prediction of VT or VF events with high accuracy. Acharya et al. used features extracted fr...

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