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

Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis