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README.md
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---
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datasets:
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- Neural-Network-Project/ECG-database
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pipeline_tag: text-classification
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tags:
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- medical
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-
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---
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language: en
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tags:
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- ecg
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- cardiology
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- medical
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- pediatric
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- time-series
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- multi-label-classification
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- tensorflow
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- cnn
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datasets:
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- Neural-Network-Project/ECG-database
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metrics:
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- f1
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- auc
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- precision
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- recall
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library_name: tensorflow
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---
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# ECG Disease Classifier - 19 Cardiac Conditions
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Multi-label classification model for detecting 19 cardiac conditions from pediatric ECG signals.
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## Model Description
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Enhanced 1D CNN with Squeeze-Excitation blocks and temporal attention for variable-length ECG classification.
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**Architecture:** 64→128→256→512 filters with residual connections
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**Training:** Focal loss for class imbalance
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**Input:** Variable-length 12-lead ECG (5-120 seconds at 500 Hz)
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## Disease Classes
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1. Fulminant/Viral Myocarditis
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2. Acute Myocarditis
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3. Myocarditis Unspecified
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4. Dilated Cardiomyopathy
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5. Hypertrophic Cardiomyopathy
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6. Cardiomyopathy Unspecified
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7. Noncompaction Ventricular Myocardium
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8. Kawasaki Disease
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9. Ventricular Septal Defect
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10. Atrial Septal Defect
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11. Atrioventricular Septal Defect
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12. Tetralogy of Fallot
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13. Pulmonary Valve Stenosis
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14. Patent Ductus Arteriosus
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15. Pulmonary Artery Stenosis
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16. Pulmonary Valve Regurgitation
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17. Mitral Valve Insufficiency
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18. Congenital Heart Malformation
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19. Healthy
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## Performance
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- F1-Score (Macro): 0.0127
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- F1-Score (Micro): 0.0849
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- Subset Accuracy: 0.0559
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## Intended Use
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⚠️ **Research and educational purposes only** - NOT for clinical diagnosis
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## Training Details
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- Batch Size: 128
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- Epochs: 17
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- Loss: Focal Loss (α=0.25, γ=2.0)
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- Optimizer: Adam (lr=0.0002)
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## Citation
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```bibtex
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@misc{ecg-classifier-2025,
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author = {Neural-Network-Project},
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title = {ECG Disease Classifier},
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year = {2025},
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publisher = {Hugging Face},
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url = {https://huggingface.co/Neural-Network-Project/ECG-Disease-Classifier}
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}
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```
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