  Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is
clinically identified with irregular and rapid heartbeat rhythm. AF puts a
patient at risk of forming blood clots, which can eventually lead to heart
failure, stroke, or even sudden death. It is of critical importance to develop
an advanced analytical model that can effectively interpret the
electrocardiography (ECG) signals and provide decision support for accurate AF
diagnostics. In this paper, we propose an innovative deep-learning method for
automated AF identification from single-lead ECGs. We first engage the
continuous wavelet transform (CWT) to extract time-frequency features from ECG
signals. Then, we develop a convolutional neural network (CNN) structure that
incorporates ResNet for effective network training and multi-branching
architectures for addressing the imbalanced data issue to process the 2D
time-frequency features for AF classification. We evaluate the proposed
methodology using two real-world ECG databases. The experimental results show a
superior performance of our method compared with traditional deep learning
models.
