A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients.

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 bench marks 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 high-performing 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 in ference to transfer learning. RESULTS: Across both datasets, ECG FMs generally performed best, followed by DL, then feature-based classifiers. However, the difference between DL and feature-based classifiers is minimal and highly dependent on the model selected, with feature-based classifiers obtaining a very slightly higher average performance on our ICU test set but DL achieving a higher overall maximum performance. The models that achieved the top F1 score on our ICU test set were InceptionV3 with recurrence plots and a fine-tuned ECGFounder (F1 = 0.88). CONCLUSION: This study demonstrates promising potential for using AI to build an automatic patient monitoring system. SIGNIFICANCE: By publishing our labelled ICU dataset 1 and performance benchmarks, this work enables the research community to continue advancing the state-of-the-art in AF detection in the ICU environment.