  Unsupervised learning methods have become increasingly important in deep
learning due to their demonstrated large utilization of datasets and higher
accuracy in computer vision and natural language processing tasks. There is a
growing trend to extend unsupervised learning methods to other domains, which
helps to utilize a large amount of unlabelled data. This paper proposes an
unsupervised pre-training technique based on masked autoencoder (MAE) for
electrocardiogram (ECG) signals. In addition, we propose a task-specific
fine-tuning to form a complete framework for ECG analysis. The framework is
high-level, universal, and not individually adapted to specific model
architectures or tasks. Experiments are conducted using various model
architectures and large-scale datasets, resulting in an accuracy of 94.39% on
the MITDB dataset for ECG arrhythmia classification task. The result shows a
better performance for the classification of previously unseen data for the
proposed approach compared to fully supervised methods.
