metadata
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
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