metadata
license: cc-by-nc-4.0
tags:
- cardiac
- ecg
- ppg
- pcg
- foundation-model
- jepa
library_name: transformers
pipeline_tag: feature-extraction
CardioState-JEPA (shared cardiac encoder)
A single shared encoder for ECG, PPG, and PCG, trained with a delay-aware cross-modal joint-embedding predictive architecture. This repo hosts the frozen encoder used for the downstream results; it maps a waveform to a pooled cardiac code.
Usage
from transformers import AutoModel
import torch
model = AutoModel.from_pretrained("<user>/CardioState-Jepa", trust_remote_code=True).eval()
ppg = torch.randn(2, 1, 1250) # [batch, 1 ch, 10 s @ 125 Hz]
with torch.no_grad():
out = model(ppg, modality="ppg", fs=125.0)
print(out.pooler_output.shape) # (2, 768) cardiac code
modality is one of "ecg" (12 leads @ 500 Hz), "ppg" (1 ch @ 125 Hz), or
"pcg" (1 ch @ 4000 Hz); pass the matching fs.
Paper
CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation
Hamza Shafiq, Hung Manh Pham, Bin Zhu, Pan Zhou, Jun Hu, and Aaqib Saeed.
arXiv:2608.12944, 2026.
- Paper: https://arxiv.org/abs/2608.12944
- Hugging Face Papers: https://huggingface.co/papers/2608.12944
Citation
If you use CardioState-JEPA in your research, please cite:
@misc{shafiq2026cardiostatejepa,
title = {CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation},
author = {Shafiq, Hamza and Pham, Hung Manh and Zhu, Bin and Zhou, Pan and Hu, Jun and Saeed, Aaqib},
year = {2026},
eprint = {2608.12944},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}