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tags:
- ecg
- cardiovascular
- multi-label-classification
- keras
- ptb-xl
datasets:
- ptb-xl
license: mit
---
# ECG Cardiovascular Disease Classification
Multi-label classification of 5 cardiovascular superclasses
(NORM, MI, STTC, CD, HYP) from 12-lead ECG recordings, trained on PTB-XL.
**Deployed model**: CNN (noaug training variant)
## Files
- `ecg_model.keras` | trained model
- `normalisation_params.npz` | per-channel mean and std (z-score, from training fold)
- `thresholds.json` | per-class decision thresholds optimised on the validation fold
## Usage
```python
import keras, numpy as np, json
from huggingface_hub import hf_hub_download
model = keras.saving.load_model(
hf_hub_download("Steenslid/ecg-ptbxl-classification", "ecg_model.keras"))
params = np.load(hf_hub_download("Steenslid/ecg-ptbxl-classification", "normalisation_params.npz"))
with open(hf_hub_download("Steenslid/ecg-ptbxl-classification", "thresholds.json")) as f:
thresholds = json.load(f)
# Input x: (1000, 12) float32 ECG in mV, 100 Hz, standard 12-lead order
x_norm = (x - params["mean"]) / params["std"]
probs = model.predict(x_norm[np.newaxis])[0]
preds = {sc: probs[i] >= thresholds[sc] for i, sc in enumerate(
["NORM","MI","STTC","CD","HYP"])}
```
**Authors:** Edvard Vindenes Steenslid & Morten Kvamme
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