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chiangfish/beat-age
beat-age is a machine learning model from chiangfish. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
This is the official checkpoint release for the paper:
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Updated May 31, 2026
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From the Hugging Face model README
This is the official checkpoint release for the paper:
Beat-Level Electrocardiographic Biological Age and Its Variability as Digital Biomarkers for Cardiovascular Risk Stratification
Official GitHub repository: https://github.com/chiangfish/beat-age
v1_best.pth: Beat-age beat-level Net1D checkpoint trained on the UK Biobank Development Cohort.ckpt_manifest.json: checkpoint metadata, including file size, SHA-256 checksum, architecture, and intended use.Beat-age is a beat-level ECG biological age model. It predicts biological age from individual segmented 12-lead cardiac cycles and aggregates beat-level predictions at the ECG-recording level.
Net1D)Download the checkpoint and place it under ckpts/ in the GitHub repository:
mkdir -p ckpts
hf download chiangfish/beat-age v1_best.pth --local-dir ckpts
Then run the inference scripts from the GitHub repository following its README.
The model was developed using controlled-access UK Biobank ECG data. Downstream external validation used MIMIC-IV-ECG. These datasets are not redistributed in this model repository.
@article{beatage2026,
title = {Beat-Level Electrocardiographic Biological Age and Its Variability as Digital Biomarkers for Cardiovascular Risk Stratification},
author = {Zirui Jiang, Guangkun Nie, Qinghao Zhao, and Shenda Hong},
year = {2026}
}