Towards Deep Learning Surrogate for the Forward Problem in Electrocardiology: A Scalable Alternative to Physics-Based Models

Researchers propose a deep learning framework to replace traditional physics-based models for solving the forward problem in electrocardiology—predicting body surface ECG signals from cardiac electrical activity. The model achieves 99% accuracy while dramatically reducing computational time, offering potential for real-time clinical applications and digital twin development.

This research addresses a fundamental bottleneck in computational cardiology. Physics-based solvers like bidomain and monodomain equations provide accurate simulations of cardiac electrophysiology but demand substantial computational resources, making real-time clinical deployment impractical. The proposed deep learning surrogate model circumvents this limitation by learning the mapping between cardiac voltage propagation and surface ECG signals, achieving near-perfect accuracy with minimal latency.

The technical approach combines attention-based sequence-to-sequence architecture with hybrid loss functions that preserve both temporal dynamics and frequency-domain characteristics. By training on diverse tissue conditions—healthy, fibrotic, and gap junction-remodelled—the model demonstrates generalization capability across physiologically relevant scenarios. This breadth reflects genuine clinical relevance rather than narrow laboratory performance.

For healthcare and biotech sectors, this work has immediate implications. Faster ECG prediction enables real-time diagnostic support systems, personalized treatment planning, and accelerated drug screening pipelines. The framework also facilitates digital twin development, where virtual cardiac models can be updated and queried at speeds compatible with continuous patient monitoring.

The broader impact extends to medical device companies and clinical AI platforms seeking to integrate sophisticated cardiac modeling into production systems. While this is a proof-of-concept, successful translation would compress computational requirements by orders of magnitude, reducing infrastructure costs and enabling deployment in resource-constrained settings. Future work should focus on three-dimensional simulations, validation against clinical patient data, and regulatory pathway clarification for clinical-grade deployment.

• →Deep learning surrogate achieves 99% R² accuracy while dramatically reducing computational cost versus physics-based solvers
• →Hybrid loss function combining Huber loss and spectral entropy preserves both temporal and frequency-domain signal fidelity
• →Model generalizes across healthy and pathological tissue conditions including fibrosis and gap junction remodelling
• →Framework enables real-time ECG prediction suitable for clinical monitoring and digital twin applications
• →Attention-based architecture with convolutional encoders validated through ablation studies as key performance drivers