ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning

• URL: http://arxiv.org/abs/2306.06340v1
• Date: Sat, 10 Jun 2023 04:23:08 GMT
• Title: ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
• Authors: Seokmin Choi, Sajad Mousavi, Phillip Si, Haben G. Yhdego, Fatemeh Khadem, Fatemeh Afghah
• Abstract summary: ECGBERT is a self-supervised representation learning approach that unlocks the underlying language of ECGs. We demonstrate ECGBERT's potential to achieve state-of-the-art results on a wide variety of tasks.
• Score: 6.0106590095197605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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