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

TL;DR

GEASI is a novel method that identifies the earliest activation sites in cardiac models using a geodesic-based approach, aiding personalized diagnosis and treatment of cardiac conditions.

Contribution

It introduces a new geodesic-based framework for simultaneous identification of all earliest activation sites in cardiac models, adaptable to various data types.

Findings

Effective in 2D and 3D in-silico models

Successful application to in-vivo intracardiac recordings

Potential for clinical use in personalized cardiac modeling

Abstract

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.