Abstract
Objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse health effects. In this study, we publish a labelled ICU dataset and benchmarks for AF detection. Methods: We compared machine learning models across three data-driven artificial intelligence (AI) approaches: feature-based classifiers, deep learning (DL), and ECG foundation models (FMs). This comparison addresses a critical gap in the literature and aims to pinpoint which AI approach is best for accurate AF detection. Electrocardiograms (ECGs) from a Canadian ICU and the 2021 PhysioNet/Computing in Cardiology Challenge were used to conduct the experiments. Multiple training configurations were tested, ranging from zero-shot inference to transfer learning. Results: On average and across both datasets, ECG FMs performed best, followed by DL, then feature-based classifiers. The model that achieved the top F1 score on our ICU test set was ECG-FM through a transfer learning strategy (F1=0.89). Conclusion: This study demonstrates promising potential for using AI to build an automatic patient monitoring system. Significance: By publishing our labelled ICU dataset (LinkToBeAdded) and performance benchmarks, this work enables the research community to continue advancing the state-of-the-art in AF detection in the ICU.
Abstract (translated)
目标：心房颤动（AF）是重症监护病房（ICU）患者中最常见的心脏心律失常，并可能导致不良健康影响。本研究发布了一个标记的ICU数据集和用于检测AF的基准。 方法：我们比较了三种基于数据的人工智能（AI）方法中的机器学习模型：特征分类器、深度学习（DL）和心电图基础模型（FMs）。这种对比解决了文献中一个关键空白，并旨在确定哪种AI方法最适合准确地检测AF。实验使用了来自加拿大ICU的心电图（ECG）数据以及2021年PhysioNet/计算心脏病学挑战赛的数据进行。我们测试了多种训练配置，从零样本推理到迁移学习。 结果：在两个数据集的平均表现中，心电图FMs表现出最佳效果，其次是深度学习方法，最后是特征分类器。在我们的ICU测试集中获得最高F1分数的模型是通过转移学习策略进行的心电图-FM（F1=0.89）。 结论：本研究表明使用AI建立自动患者监测系统具有巨大潜力。 重要意义：通过发布标记的ICU数据集（链接待添加）和性能基准，这项工作使研究社区能够继续推进ICU中AF检测领域的最新技术水平。
URL
https://arxiv.org/abs/2512.18031
