ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised
  Representation Learning
Seokmin Choi Sajad Mousavi Phillip Si Haben G. Yhdego Fatemeh Khadem Fatemeh Afghah
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.