Atrial Fibrillation Risk Prediction from Electrocardiogram and Related Health Data with Deep Neural Network

Abstract

Electrocardiography (ECG) is a widely used tool for studying and diagnosing the heart diseases. Atrial fibrillation (AF) is an irregular and often rapid heart rate that can increase the risk of strokes, heart failure and other heart-related complications. In this study, we develop a novel and effective method to predict the potential AF risk of patients using our ECG signal dataset collected in the University of Illinois Hospital and Health Sciences System. We use a convolutional neural network (CNN) structure to process both the ECG signals and the related health data of patients. Our experimental results indicate that the model with patients' health data can predict the AF with 79.9% accuracy), and which is better than a CNN trained without related health data 72.2% accuracy), which implies that patients' health data play an important role in predicting AF risk. Very high sensitivity and specificity of the class of normal sinus rhythm (NSR) cases also verify that the model works well for distinguishing between NSR and ECG signals with potential AF risk.