  In the medical field, current ECG signal analysis approaches rely on
supervised deep neural networks trained for specific tasks that require
substantial amounts of labeled data. However, our paper introduces ECGBERT, a
self-supervised representation learning approach that unlocks the underlying
language of ECGs. By unsupervised pre-training of the model, we mitigate
challenges posed by the lack of well-labeled and curated medical data. ECGBERT,
inspired by advances in the area of natural language processing and large
language models, can be fine-tuned with minimal additional layers for various
ECG-based problems. Through four tasks, including Atrial Fibrillation
arrhythmia detection, heartbeat classification, sleep apnea detection, and user
authentication, we demonstrate ECGBERT's potential to achieve state-of-the-art
results on a wide variety of tasks.
