Metadata-Version: 2.4
Name: ynet-cuffless-bp
Version: 0.1.0
Summary: Y-NET: Cuffless ABP from ECG & PPG with BPV & ASI markers
Home-page: https://github.com/jokerjnr/ynet
Author: Jokerjnr & Emefa A. Apedo
Author-email: mcgovernowusubekoe@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.19.0
Requires-Dist: onnxruntime>=1.10.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
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Dynamic: summary

# Y-NET: Cuffless Blood Pressure Estimation

Deep learning model for ABP reconstruction from ECG & PPG with clinical markers.

## Install

```bash
pip install ynet-abp
```

## Use

```python
from ynet_abp import YNETInference, compute_bpv, compute_asi
import numpy as np

# Load ONNX model
model = YNETInference('ynet_model.onnx')

# Predict
ecg = np.random.randn(512)
ppg = np.random.randn(512)
result = model.predict(ecg, ppg)

# Clinical markers
sbp_list = [120, 121, 122]
dbp_list = [80, 81, 80]
print(f"BPV: {compute_bpv(sbp_list):.2f}")
print(f"ASI: {compute_asi(sbp_list, dbp_list):.3f}")
```

## Citation

Jokerjnr & Apedo, E. A. (2026). Y-NET: ABP Reconstruction from Dual-Channel Biosignals. ISH-ECS.
