AI 中文总结
本研究提出FOUND-AF基准框架，在统一条件下评估9种心电基础模型在4种数据集上的心房颤动检测性能，发现ECGFounder综合表现最优，为相关模型选择提供可复现方案。
AI 中文摘要
心房颤动（AF）是最常见的持续性心律失常，与中风、心力衰竭和死亡风险升高相关。近期的心电（ECG）基础模型为自动化AF检测提供了可迁移的表示，但由于现有研究使用不同的数据集、预处理流程、分类器和验证协议，其相对有效性尚不明确。本研究提出了FOUND-AF，这是一个统一的、控制数据泄漏的、面向部署的基准测试框架，用于在相同实验条件下评估预训练心电表示的质量。研究评估了来自五个家族的九个公开基础模型，包括HuBERT-ECG、CLEF、ST-MEM、ECG-JEPA和ECGFounder，涉及四个异构心电数据集：AFDB、CinC2017、CPSC2021和LTAFDB。所有模型均作为冻结特征提取器，采用标准化预处理、模型原生重采样、固定XGBoost分类器，以及记录级分组交叉验证。评估内容包括分类指标、受试者工作特征分析、带Holm校正的配对记录级自举比较、嵌入空间可视化和计算效率分析。ECGFounder模型在所有数据集上始终实现最强的整体性能，同时在准确率、模型规模、推理时间和内存使用之间提供了良好的权衡。因此，FOUND-AF为选择心电基础模型提供了可复现的框架，并证明紧凑的、经临床预训练的编码器可在异构采集场景中支持稳健且计算高效的AF检测。
英文摘要
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality. Recent ECG foundation models offer transferable representations for automated AF detection. However, their relative effectiveness remains unclear because existing studies use different datasets, preprocessing procedures, classifiers, and validation protocols. This study presents FOUND-AF, a unified, leakage-controlled, and deployment-oriented benchmarking framework that evaluates the quality of pretrained ECG representations under identical experimental conditions. Nine publicly available foundation models from five families, including HuBERT-ECG, CLEF, ST-MEM, ECG-JEPA, and ECGFounder, were evaluated across four heterogeneous ECG datasets, namely AFDB, CinC2017, CPSC2021, and LTAFDB. All models were used as frozen feature extractors with standardized preprocessing, model-native resampling, a fixed XGBoost classifier, and recording-level grouped cross-validation. The evaluation included classification metrics, receiver operating characteristic analysis, paired recording-level bootstrap comparisons with Holm correction, embedding-space visualization, and computational efficiency profiling. The ECGFounder model consistently achieved the strongest overall performance across datasets while offering a favorable trade-off between accuracy, model size, inference time, and memory usage. FOUND-AF therefore provides a reproducible framework for selecting ECG foundation models and demonstrates that compact, clinically pretrained encoders can support robust and computationally efficient AF detection across heterogeneous acquisition settings.
