Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,105 @@
+---
+license: apache-2.0
+tags:
+- medical
+- ecg
+- cardiology
+- classification
+- pytorch
+- trustcat
+datasets:
+- ptb-xl
+metrics:
+- f1
+pipeline_tag: audio-classification
+---
+
+# QueenBee-ECG Classifier
+
+**1D ResNet for 12-lead ECG diagnostic classification on PTB-XL**
+
+Part of the TrustCat sovereign medical AI stack.
+
+## Model Description
+
+Classifies 12-lead ECGs into 5 diagnostic superclasses:
+
+| Class | Description | Test F1 |
+|-------|-------------|---------|
+| NORM | Normal ECG | 81% |
+| MI | Myocardial Infarction | 62% |
+| STTC | ST-T Changes | 58% |
+| CD | Conduction Disturbance | 57% |
+| HYP | Hypertrophy | 31% |
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Macro F1 | 58% |
+| Accuracy | 67% |
+| Weighted F1 | 68% |
+
+## Architecture
+
+- **Type**: 1D ResNet
+- **Parameters**: 8.7M
+- **Input**: 12-lead ECG (1000 samples @ 100Hz = 10 seconds)
+- **Output**: 5-class probability distribution
+
+## Training
+
+- **Dataset**: PTB-XL (17,084 train / 2,146 val / 2,158 test)
+- **Hardware**: 2x RTX 5090
+- **Epochs**: 18 (early stopping)
+- **Training Time**: ~3 minutes
+- **Optimizer**: AdamW
+- **Loss**: Cross-entropy with class weights
+
+## Usage
+
+```python
+import torch
+import wfdb
+from model import ECGResNet  # See training script
+
+# Load model
+model = ECGResNet(n_classes=5)
+checkpoint = torch.load("best_model.pt")
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+# Load ECG (12-lead, 10 seconds @ 100Hz)
+signal, _ = wfdb.rdsamp("path/to/ecg")
+signal = (signal - signal.mean(0)) / (signal.std(0) + 1e-8)
+x = torch.tensor(signal.T, dtype=torch.float32).unsqueeze(0)
+
+# Predict
+with torch.no_grad():
+    logits = model(x)
+    pred = logits.argmax(dim=1).item()
+
+classes = ["NORM", "MI", "STTC", "CD", "HYP"]
+print(f"Prediction: {classes[pred]}")
+```
+
+## Intended Use
+
+- Clinical decision support
+- ECG screening assistance
+- Cardiology research
+
+## Limitations
+
+- Trained on PTB-XL dataset only
+- Not FDA cleared
+- HYP class has weak performance (small training set)
+- Requires clinical validation
+
+## License
+
+Apache 2.0
+
+---
+
+**Built with diamond hands by TrustCat - Sovereign Medical AI**