Clinical ECG Classifier
A multi-branch deep learning model for automated ECG classification and clinical interpretation.
Model Description
This is a PyTorch-based ECG classification model that uses a multi-branch architecture combining:
- Rhythm Branch: 1D ResNet for temporal rhythm analysis
- Morphology Branch: 2D CNN for local morphology patterns
- Global Branch: 2D CNN for global ECG interpretation
The model is trained to classify 71 different ECG conditions including arrhythmias, conduction disorders, ischemia, myocardial infarction, and hypertrophy patterns.
Performance
- Macro AUC: 0.9424
- F-max Score: 0.7573
- Training Dataset: PTB-XL (21,799 ECGs)
- Test Performance: Evaluated on 2,198 test samples
Model Architecture
The model combines three specialized branches:
- Rhythm Branch (1D ResNet): Analyzes temporal patterns for rhythm detection
- Morphology Branch (2D CNN): Focuses on local morphological features
- Global Branch (2D CNN): Captures global ECG patterns
All branches are fused through a final classifier with dropout regularization.
Input Format
- Input Shape: [batch_size, 12, 1000]
- Sampling Rate: 100 Hz
- Duration: 10 seconds
- Leads: Standard 12-lead ECG (I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6)
Supported Conditions
The model can classify 71 different ECG conditions including:
Rhythm Abnormalities
- Atrial Fibrillation (AFIB)
- Atrial Flutter (AFL)
- Sinus Tachycardia (STACH)
- Sinus Bradycardia (SBRAD)
- Premature Ventricular Contractions (PVC)
- Ventricular Tachycardia (VTAC)
Conduction Disorders
- Bundle Branch Blocks (LBBB, RBBB)
- AV Blocks (1st, 2nd, 3rd degree)
- Fascicular Blocks (LAFB, LPFB)
Ischemia & Infarction
- ST-T Changes (STTC)
- Myocardial Infarction patterns (AMI, IMI, LMI)
- Ischemia patterns (ISCAN, ISCIN, ISCLA)
Hypertrophy
- Left Ventricular Hypertrophy (LVH)
- Right Ventricular Hypertrophy (RVH)
- Atrial Enlargement (LAE, RAE)
Usage
1import torch
2import numpy as np
3
4# Load the model
5model = torch.load('ecg_model.pth', map_location='cpu')
6model.eval()
7
8# Prepare ECG data (12 leads, 1000 samples)
9ecg_data = np.random.randn(1, 12, 1000) # Replace with actual ECG data
10ecg_tensor = torch.tensor(ecg_data, dtype=torch.float32)
11
12# Make prediction
13with torch.no_grad():
14 logits = model(ecg_tensor)
15 probabilities = torch.sigmoid(logits)
16
17# Get top predictions
18top_indices = torch.topk(probabilities, k=5).indices[0]
Clinical Applications
This model can assist healthcare professionals in:
- Automated ECG Screening: Rapid identification of abnormal ECGs
- Clinical Decision Support: Highlighting potential cardiac conditions
- Educational Tool: Teaching ECG interpretation patterns
- Research: Large-scale ECG analysis for epidemiological studies
Limitations
- Model trained on PTB-XL dataset - performance may vary on other populations
- Not a replacement for clinical judgment
- Requires standard 12-lead ECG format
- Should be used as a supportive tool alongside clinical assessment
Technical Details
- Framework: PyTorch
- Model Size: ~166MB
- Training: 25 epochs with AdamW optimizer
- Regularization: Dropout, batch normalization
- Loss Function: Binary cross-entropy with logits
Citation
If you use this model in your research, please cite:
1@misc{clinical-ecg-classifier,
2 title={Clinical ECG Classifier: Multi-branch Deep Learning for ECG Analysis},
3 author={[Abdul Zacky, Irma Nia Alwijah, Muttaqin Muzakkir]},
4 year={2024},
5 url={https://huggingface.co/clinical-ecg-classifier}
6}
License
This model is provided for research and educational purposes. Please ensure compliance with applicable medical device regulations before clinical use.
Contact
For questions or issues, please contact:
zacxvan@gmail.com
