---
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.