# GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models

arXiv: https://arxiv.org/abs/2102.09962 · HTML: https://mlsanity.com/paper/2102.09962/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2021.

## Built on by (most cited first, 9 of 9)

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- [Neural Eulerian Scene Flow Fields](https://mlsanity.com/paper/2410.02031/) (2024)
- [Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey](https://mlsanity.com/paper/2406.11445/) (2024)
- [Deep Computational Model for the Inference of Ventricular Activation Properties](https://mlsanity.com/paper/2208.04028/) (2022)
- [Frequency-Aware Density Control via Reparameterization for High-Quality Rendering of 3D Gaussian Splatting](https://mlsanity.com/paper/2503.07000/) (2025)
- [Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds](https://mlsanity.com/paper/2203.06222/) (2022)
- [Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach](https://mlsanity.com/paper/2308.08410/) (2023)
- [Smoothness and continuity of cost functionals for ECG mismatch computation](https://mlsanity.com/paper/2201.04487/) (2022)
- [Ensemble learning of the atrial fiber orientation with physics-informed neural networks](https://mlsanity.com/paper/2410.23388/) (2024)

## Abstract (arXiv)

The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet, their number, location and timing may not be easily inferred from remote recordings, such as the epicardial activation or the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model. In this work, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify all EASs. To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton--Jacobi formulation to minimize a given objective function, e.g. the quadratic mismatch to given activation measurements. This versatile approach can be extended to estimate the number of activation sites by means of the topological gradient, or fitting a given ECG. We conducted various experiments in 2D and 3D for in-silico models and an in-vivo intracardiac recording collected from a patient undergoing cardiac resynchronization therapy. The results demonstrate the clinical applicability of GEASI for potential future personalized models and clinical intervention.