Key result
Single-lead ECG foundation models accurately detect AF and AFL events across diverse datasets.
Why the study?
Existing deep learning models for automated ECG analysis are often constrained by small, task-specific datasets and lack generalization.
Does transfer learning with a pretrained foundation model accurately detect atrial fibrillation and flutter on single-lead ECG?
Does transfer learning with a pretrained foundation model accurately detect atrial fibrillation and flutter on single-lead ECG?
A fine-tuned pretrained ECG foundation model can accurately detect atrial fibrillation and flutter from single-lead ECGs, overcoming limitations of small datasets and class imbalance.
Answers are grounded in this paper and cite their sources.
May support single-lead AF detection via foundation models; leaves open prospective validation before clinical use.
Atrial fibrillation (AF) is a prevalent arrhythmia that increases the risk of stroke and heart failure. Early detection of AF and related conditions, such as atrial flutter (AFL), is challenging due to their transient nature and subtle electrocardiogram (ECG) characteristics. Although deep learning has shown potential for automated ECG analysis, existing models are often constrained by small, task-specific datasets and lack generalization. In this study, we fine-tune a pretrained ECG foundation model using single-lead ECG data for arrhythmia classification. We conduct experiments on multiple publicly available datasets and demonstrate that transfer learning with single-lead input effectively detects AF/AFL events with high accuracy. Furthermore, we specifically address the challenge of class imbalance using weighted loss and demonstrate the model’s robustness across diverse real-world datasets.
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Lee et al. (2025) studied Atrial fibrillation and atrial flutter. Pretrained ECG foundation model with transfer learning was evaluated on AF/AFL event detection. Transfer learning with a pretrained ECG foundation model using single-lead input effectively detected atrial fibrillation and flutter events with high accuracy across diverse datasets.
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