---
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**