Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models

Electrical waves in the heart form rotating spiral or scroll waves during life-threatening arrhythmias such as atrial or ventricular fibrillation. The wave dynamics are typically modeled using coupled partial differential equations, which describe reaction-diffusion dynamics in excitable media. More recently, data-driven generative modeling has emerged as an alternative to generate spatio-temporal patterns in physical and biological systems. Here, we explore denoising diffusion probabilistic models for the generative modeling of electrical wave patterns in cardiac tissue. We trained diffusion models with simulated electrical wave patterns to be able to generate such wave patterns in unconditional and conditional generation tasks. For instance, we explored inpainting tasks, such as reconstructing three-dimensional wave dynamics from superficial two-dimensional measurements, and evolving and generating parameter-specific dynamics. We characterized and compared the diffusion-generated solutions to solutions obtained with biophysical models and found that diffusion models learn to replicate spiral and scroll waves dynamics so well that they could serve as an alternative data-driven approach for the modeling of excitation waves in cardiac tissue. For instance, we found that it is possible to initiate ventricular fibrillation (VF) dynamics instantaneously without having to apply pacing protocols in order to induce wavebreak. The VF dynamics can be created in arbitrary ventricular geometries and can be evolved over time. However, we also found that diffusion models `hallucinate' wave patterns when given insufficient constraints. Regardless of these limitations, diffusion models are an interesting and powerful tool with many potential applications in cardiac arrhythmia research and diagnostics.