Deep-Learning-Assisted Cardiac Electrophysiology Simulation

Simulation built upon partial and ordinary differential equations has been a classic approach to modeling cardiac electrophysiological dynamics. However, mitigating the computational burden of differential equations is still a challenging problem. This paper provides a novel alternative utilizing data-driven recurrent neural networks for cardiac electrophysiological dynamic simulation. Specifically, we develop a long short-term memory (LSTM)-assisted simulation to capture the underlying dynamics of cardiac electrophysiology while preserving computational efficiency. Experimental results demonstrate the efficiency and effectiveness of the proposed method, which outperforms the differential equation-based simulation approach while significantly reducing the computational cost. The proposed method offers a promising alternative to traditional simulation and may contribute to the development of more efficient and accurate approaches for simulating cardiac electrophysiology.