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.
@article{arxiv.2306.06340,
title = {ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning},
author = {Seokmin Choi and Sajad Mousavi and Phillip Si and Haben G. Yhdego and Fatemeh Khadem and Fatemeh Afghah},
journal= {arXiv preprint arXiv:2306.06340},
year = {2023}
}