ArticleMedical & biological engineering & computing2018
Quantifying the effect of uncertainty in input parameters in a simplified bidomain model of partial thickness ischaemia.
Article in Medical & biological engineering & computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 28 citations in OpenAlex.
- Uncertainty quantification via polynomial chaos expansion of myocardial fibre orientation and cardiac activation patterns.The Journal of physiology · 2026Article
- Generating patient-specific computational models with point cloud data from human atrial electrophysiology studies.PloS one · 2026Article
- Progress of Conductivity and Conduction Velocity Measured in Human and Animal Hearts.Reviews in cardiovascular medicine · 2024Review
- Extracellular Perinexal Separation Is a Principal Determinant of Cardiac Conduction.Circulation research · 2023Article
- Improving the accuracy of retrieved cardiac electrical conductivities.The ANZIAM journal · 2022Article
- A modified approach to determine the six cardiac bidomain conductivities.Computers in biology and medicine · 2021Article
- Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning.International journal for numerical methods in biomedical engineering · 2021Article
- Approaches for determining cardiac bidomain conductivity values: progress and challenges.Medical & biological engineering & computing · 2020Review
- Article
- Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators.Frontiers in physiology · 2020Article
- Computational models in cardiology.Nature reviews. Cardiology · 2019Review
- Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia.Frontiers in physiology · 2019Article
- Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.Medical image analysis · 2018Article
- Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation.Frontiers in physiology · 2018Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Reduced blood flow in the coronary arteries can lead to damaged heart tissue (myocardial ischaemia). Although one method for detecting myocardial ischaemia involves changes in the ST segment of the electrocardiogram, the relationship between these changes and subendocardial ischaemia is not fully understood. In this study, we modelled ST-segment epicardial potentials in a slab model of cardiac ventricular tissue, with a central ischaemic region, using the bidomain model, which considers conduction longitudinal, transverse and normal to the cardiac fibres. We systematically quantified the effect of uncertainty on the input parameters, fibre rotation angle, ischaemic depth, blood conductivity and six bidomain conductivities, on outputs that characterise the epicardial potential distribution. We found that three typical types of epicardial potential distributions (one minimum over the central ischaemic region, a tripole of minima, and two minima flanking a central maximum) could all occur for a wide range of ischaemic depths. In addition, the positions of the minima were affected by both the fibre rotation angle and the ischaemic depth, but not by changes in the conductivity values. We also showed that the magnitude of ST depression is affected only by changes in the longitudinal and normal conductivities, but not by the transverse conductivities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.