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AI Research
arXiv CS.AI·Jun 11·5 min read
Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models
TL;DR
The forward problem in electrocardiology, which calculates body surface potentials from heart activity, is computationally intensive with traditional physics-based models. A deep learning framework was developed as a surrogate to these models, utilizing a time-dependent, attention-based structure.
✦ Why It Matters
Engineers can leverage deep learning to create faster, scalable solutions for real-time electrocardiology applications.
Key Takeaways
- Explore deep learning as a surrogate for traditional physics-based models.
- Implement attention-based architectures for time-dependent predictions.
- Utilize hybrid loss functions to improve model accuracy.
- Test models on diverse cardiac conditions for robustness.
How It Works
The proposed deep learning framework employs a sequence-to-sequence architecture that leverages attention mechanisms to focus on relevant parts of the input data, allowing it to effectively predict ECG signals from cardiac voltage maps. The hybrid loss function combines Huber loss, which is robust to outliers, with a spectral entropy term to ensure the model captures both temporal dynamics and frequency characteristics of the ECG signals.