license: mit
library_name: pytorch
tags:
- ecg
- biological-age
- cardiology
- pytorch
- uk-biobank
Beat-age
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
Files
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.
Model
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.
- Architecture: one-dimensional residual CNN (
Net1D) - Input: segmented 12-lead ECG beats
- Output: predicted biological age in years
- Age gap: predicted age minus chronological age
Usage
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.
Data
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.
Citation
@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}
}