README.md

---
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

```python
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:

```bibtex
@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}
}
```