Atrial Fibrillation Detection Using Convolutional Neural Networks

-Atrial fibrillation (AF) is the most common cardiac arrhythmia. AF may lead to stroke, heart failure, sudden death and increase the risk of cardiovascuar morbidity. Furthermore, AF draws great attention in clinical practice because of its continuously growing prevalence in aging society. The features for AF diagnosis include absolutely irregular RR intervals, and no discernible and distinct P waves. Paroxysmal AF is usually transient and hard to be found in routine health check. Longterm ECG monitoring may raise the sensitivity of AF’s detection. However, the analysis of huge amount of ECG is time and cost consuming. In this study, we propose a method based on convolutional neural networks for the detection of AF. Through validating with MIT-BIH atrial fibrillation database, we get a sensitivity of 98.9%, a specificity of 99.0%, and an accuracy of 99.0%.