基于千万级心电图数据，构建可通用的专家级心电分析模型。
An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains
- 利用1000万+心电图和150类标注训练通用心电基础模型。
- 在内部测试中80种诊断的AUROC超0.95，外部验证表现优异。
- 支持单导联和低质量心电图，适用于移动健康监测场景。
人工智能在心电图（ECG）分析与心血管疾病评估中展现出巨大潜力。近期，基础模型在推动医疗AI发展方面发挥了重要作用。构建心电图基础模型有望将AI-ECG研究推向新高度，但面临数据量不足、跨领域泛化能力弱及单导联与多导联分析性能差距大的挑战。本文提出ECGFounder，一个基于哈佛-埃默里心电数据库中超过1000万条真实心电图、涵盖150个标签类别的通用心电基础模型，通过专家标注增强诊断能力。该模型既可直接使用，也可微调适配下游任务，具备高实用性。特别地，其在低质量心电图及单导联心电图上表现良好，适用于移动监测等场景。实验表明，ECGFounder在内部验证集上达到专家级水平，80种诊断的AUROC均超过0.95；在外部验证集上表现出强分类性能与跨诊断泛化能力。微调后，在人口统计分析、临床事件检测与跨模态心律诊断任务中优于基线模型。模型与数据将在发表后通过bdsp.io公开，代码见https://github.com/PKUDigitalHealth/ECGFounder。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model faces several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. Additionally, there is a notable performance gap between single-lead and multi-lead ECG analyses. We introduced an ECG Foundation Model (ECGFounder), a general-purpose model that leverages real-world ECG annotations from cardiology experts to broaden the diagnostic capabilities of ECG analysis. ECGFounder was trained on over 10 million ECGs with 150 label categories from the Harvard-Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis through ECG analysis. The model is designed to be both an effective out-of-the-box solution, and a to be fine-tunable for downstream tasks, maximizing usability. Importantly, we extended its application to lower rank ECGs, and arbitrary single-lead ECGs in particular. ECGFounder is applicable to supporting various downstream tasks in mobile monitoring scenarios. Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets, with AUROC exceeding 0.95 for eighty diagnoses. It also shows strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographic analysis, clinical event detection, and cross-modality cardiac rhythm diagnosis. The trained model and data will be publicly released upon publication through the bdsp.io. Our code is available at https://github.com/PKUDigitalHealth/ECGFounder
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