ArticleMedical & biological engineering & computing2014
Patient-specific generation of the Purkinje network driven by clinical measurements of a normal propagation.
Article in Medical & biological engineering & computing, 2014. 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.
- Enhanced optimization-based method for the generation of patient-specific models of Purkinje networks.Scientific reports · 2023Article
- Precision medicine in human heart modeling : Perspectives, challenges, and opportunities.Biomechanics and modeling in mechanobiology · 2021Review
- An Inverse Eikonal Method for Identifying Ventricular Activation Sequences from Epicardial Activation Maps.Journal of computational physics · 2020Article
- A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data.Medical image analysis · 2019Article
- Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia.Frontiers in physiology · 2019Article
- Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A Sensitivity Study.Cardiovascular engineering and technology · 2018Article
- Generating Purkinje networks in the human heart.Journal of biomechanics · 2016Article
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Authors and funding
10 authors.
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Abstract
The propagation of the electrical signal in the Purkinje network is the starting point for the activation of the ventricular muscular cells leading to the contraction of the ventricle. In the computational models, describing the electrical activity of the ventricle is therefore important to account for the Purkinje fibers. Until now, the inclusion of such fibers has been obtained either by using surrogates such as space-dependent conduction properties or by generating a network based on an a priori anatomical knowledge. The aim of this work was to propose a new method for the generation of the Purkinje network using clinical measures of the activation times on the endocardium related to a normal electrical propagation, allowing to generate a patient-specific network. The measures were acquired by means of the EnSite NavX system. This system allows to measure for each point of the ventricular endocardium the time at which the activation front, that spreads through the ventricle, has reached the subjacent muscle. We compared the accuracy of the proposed method with the one of other strategies proposed so far in the literature for three subjects with a normal electrical propagation. The results showed that with our method we were able to reduce the absolute errors, intended as the difference between the measured and the computed data, by a factor in the range 9-25 %, with respect to the best of the other strategies. This highlighted the reliability of the proposed method and the importance of including a patient-specific Purkinje network in computational models.
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