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README.md

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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.