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license: mit
library_name: pytorch
tags:
- ecg
- biological-age
- cardiology
- pytorch
- uk-biobank
pipeline_tag: tabular-regression
---
# 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:
```bash
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
```bibtex
@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}
}
```
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