  Cardiovascular diseases are the most common cause of mortality worldwide.
Detection of atrial fibrillation (AF) in the asymptomatic stage can help
prevent strokes. It also improves clinical decision making through the delivery
of suitable treatment such as, anticoagulant therapy, in a timely manner. The
clinical significance of such early detection of AF in electrocardiogram (ECG)
signals has inspired numerous studies in recent years, of which many aim to
solve this task by leveraging machine learning algorithms. ECG datasets
containing AF samples, however, usually suffer from severe class imbalance,
which if unaccounted for, affects the performance of classification algorithms.
Data augmentation is a popular solution to tackle this problem.
  In this study, we investigate the impact of various data augmentation
algorithms, e.g., oversampling, Gaussian Mixture Models (GMMs) and Generative
Adversarial Networks (GANs), on solving the class imbalance problem. These
algorithms are quantitatively and qualitatively evaluated, compared and
discussed in detail. The results show that deep learning-based AF signal
classification methods benefit more from data augmentation using GANs and GMMs,
than oversampling. Furthermore, the GAN results in circa $3\%$ better AF
classification accuracy in average while performing comparably to the GMM in
terms of f1-score.
