Leveraging ECG foundation models in critical care for sinus rhythm and atrial fibrillation classification.

Abstract

Background: Recent advances in deep learning have led to the development of ECG foundation models (ECG-FMs) trained with self-supervised learning, which can extract generalizable representations from large-scale data. In this study, we evaluated the performance of these ECG-FMs in detecting atrial fibrillation and sinus rhythm during continuous monitoring of intensive care unit (ICU) patients, using data from Amsterdam University Medical Center (Amsterdam UMC). Our three-stage study: (i) used within-dataset classification performance on public PhysioNet datasets as an indirect proxy for label consistency, (ii) tested off-the-shelf fine-tuned ECG-FMs on ICU data, and (iii) fine-tuned ECG-FM on curated public datasets, with and without weakly labelled in-house ICU data.

Results: Off-the-shelf model showed limited transferability (F1 = 0.68 for sinus rhythm (SR), 0.52 for atrial fibrillation (AF)). Fine-tuning on consistent public datasets achieved robust performance (F1 = 0.91 for SR, 0.82 for AF), while adding weakly labelled ICU data further improved recall at some cost to precision. Performance remained stable across varying ECG lead configurations, supporting adaptability to heterogeneous inputs.

Conclusions: These findings indicate that off-the-shelf pre-trained ECG-FMs do not generalise well to continuous monitoring in ICU populations; however, after targeted fine-tuning, they can achieve strong performance even in unseen cohorts, underscoring the importance of high-quality fine-tuning data and rigorous local validation.