A Deep Learning Method to Detect Atrial Fibrillation Based on Continuous Wavelet Transform.

Abstract

Atrial fibrillation (AF) is one of the most common arrhythmias. The automatic AF detection is of great clinical significance but at the same time it remains a big problem to researchers. In this study, a novel deep learning method to detect AF was proposed. For a 10 s length single lead electrocardiogram (ECG) signal, the continuous wavelet transform (CWT) was used to obtain the wavelet coefficient matrix, and then a convolutional neural network (CNN) with a specific architecture was trained to discriminate the rhythm of the signal. The ECG data in multiple databases were divided into 4 classes according to the rhythm annotation: normal sinus rhythm (NSR), atrial fibrillation (AF), other types of arrhythmia except AF (OTHER), and noise signal (NOISE). The method was evaluated using three different wavelet bases. The experiment showed promising results when using a Morlet wavelet, with an overall accuracy of 97.56%, an average sensitivity of 97.56%, an average specificity of 99.19%. Besides, the area under curve (AUC) value is 0.9983, which showed that the proposed method was effective for detecting AF.