ArticleInternational journal for numerical methods in biomedical engineering2021
GEASI: Geodesic-based earliest activation sites identification in cardiac models.
Article in International journal for numerical methods in biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An eikonal model with re-excitability for fast simulations in cardiac electrophysiology.The Journal of physiology · 2026Article
- An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology.Medical image analysis · 2026Article
- From bits to bedside: entering the age of digital twins in cardiac electrophysiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Review
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Graph-based homogenisation for modelling cardiac fibrosis.Journal of computational physics · 2022Article
- Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications.Biophysics reviews · 2021Article
- GEASI: Geodesic-based earliest activation sites identification in cardiac models.International journal for numerical methods in biomedical engineering · 2021Article
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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, for example, 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.
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