Peking University (PKUDigitalHealth) / Harvard-Emory
Large-scale ECG foundation model pretrained on more than 10 million recordings spanning 150 label categories from the Harvard-Emory ECG Database. Built as a general-purpose feature extractor that can be fine-tuned for arrhythmia detection, demographic inference, and event prediction, and externally validated on MIMIC-IV-ECG and PTB-XL. Also used as the pretrained backbone for downstream clinical models such as Pocket-K, a hyperkalemia detector. Developed by Peking University and Harvard-Emory researchers.
Architecture
CNN (1D)
Net1D CNN with RegNet-inspired stage-wise channel scaling; positive-label augmentation to handle incomplete annotations
Framework
PyTorch
Added to catalog
2026-07-10
MIT
License for model weights only. Associated code may be licensed seperately, check code source for specific terms.
Harvard-Emory ECG Database
Over 10 million ECG recordings; unique patient count not stated in source paper
Multi-label classification