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---
tags:
- ecg
- multi-label-classification
- medical
- cardiology
library_name: tensorflow
---
# ECG Multi-Label Classification Model
This model performs multi-label classification on ECG signals to detect:
- Myocarditis
- Cardiomyopathy
- Kawasaki Disease
- Congenital Heart Disease (CHD)
- Healthy
## Model Architecture
- 1D CNN with 4 convolutional blocks
- Input: 12-lead ECG (5000 samples × 12 leads)
- Output: 5 sigmoid outputs (multi-label)
## Training
- Framework: TensorFlow/Keras
- Optimizer: Adam
- Loss: Binary Crossentropy
- Dataset: Pediatric ECG database
## Usage
```python
import tensorflow as tf
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(
repo_id="Neural-Network-Project/ECG-models",
filename="checkpoint_final.keras"
)
# Load model
model = tf.keras.models.load_model(model_path)
# Predict (input shape: [batch_size, 5000, 12])
predictions = model.predict(ecg_data)
```
## Classes
0. Myocarditis
1. Cardiomyopathy
2. Kawasaki Disease
3. CHD
4. Healthy
## Citation
Please cite this model if you use it in your research.
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