Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds
This addresses a critical need for cardiologists to identify potentially dangerous heart arrhythmias in real-time clinical settings, though it appears incremental as it builds on existing optimization and modeling techniques.
The paper tackles the problem of non-invasively localizing ectopic activation sites in the heart from 12-lead ECG data by formulating it as a global optimization problem, achieving convergence in 11.7±10.4 iterations for single-fidelity and 3.5±1.7 iterations for multi-fidelity cases.
We propose a method for identifying an ectopic activation in the heart non-invasively. Ectopic activity in the heart can trigger deadly arrhythmias. The localization of the ectopic foci or earliest activation sites (EASs) is therefore a critical information for cardiologists in deciding the optimal treatment. In this work, we formulate the identification problem as a global optimization problem, by minimizing the mismatch between the ECG predicted by a cardiac model, when paced at a given EAS, and the observed ECG during the ectopic activity. Our cardiac model amounts at solving an anisotropic eikonal equation for cardiac activation and the forward bidomain model in the torso with the lead field approach for computing the ECG. We build a Gaussian process surrogate model of the loss function on the heart surface to perform Bayesian optimization. In this procedure, we iteratively evaluate the loss function following the lower confidence bound criterion, which combines exploring the surface with exploitation of the minimum region. We also extend this framework to incorporate multiple levels of fidelity of the model. We show that our procedure converges to the minimum only after $11.7\pm10.4$ iterations (20 independent runs) for the single-fidelity case and $3.5\pm1.7$ iterations for the multi-fidelity case. We envision that this tool could be applied in real time in a clinical setting to identify potentially dangerous EASs.