From Hospital to Portables: A Universal ECG Foundation Model Built on 10+ Million Diverse Recordings

Artificial Intelligence (AI) has shown great promise in electrocardiogram (ECG) analysis and cardiovascular disease detection. However, developing a general AI-ECG model has been challenging due to inter-individual variability and the diversity of ECG diagnoses, limiting existing models to specific diagnostic tasks and datasets. Moreover, current AI-ECG models struggle to achieve comparable performance between single-lead and 12-lead ECGs, limiting the application of AI-ECG to portable and wearable ECG devices. To address these limitations, we introduce 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 is 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 effective out-of-the-box and fine-tunable for downstream tasks, maximizing usability. More importantly, we extend its application to single-lead ECGs, enabling complex condition diagnoses and supporting various downstream tasks in mobile and remote monitoring scenarios. Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets for both 12-lead and single-lead ECGs, while also exhibiting strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographics detection, 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/bdsp-core/ECGFounder.