Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models
Lead Research Organisation:
University of Sheffield
Department Name: Computer Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Corrado C
(2023)
Quantifying the impact of shape uncertainty on predicted arrhythmias.
in Computers in biology and medicine
Coveney S
(2021)
Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators.
in Frontiers in physiology
Coveney S
(2020)
Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators.
in Frontiers in physiology
Coveney S
(2020)
Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds.
in IEEE transactions on bio-medical engineering
Bernjak A
(2021)
Hypoglycaemia combined with mild hypokalaemia reduces the heart rate and causes abnormal pacemaker activity in a computational model of a human sinoatrial cell.
in Journal of the Royal Society, Interface
Corrado C
(2020)
Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions.
in Medical image analysis
Clayton RH
(2020)
An audit of uncertainty in multi-scale cardiac electrophysiology models.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Coveney S
(2020)
Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Lei CL
(2020)
Considering discrepancy when calibrating a mechanistic electrophysiology model.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Coveney S
(2018)
Fitting two human atrial cell models to experimental data using Bayesian history matching.
in Progress in biophysics and molecular biology
| Description | We have implemented a novel method that enables us to take a small number of noisy measurements of electrical activation in the heart, and use them to interpolate and extrapolate activation times over the surface of the left atrium. This method is novel because there it accounts for uncertainties in the measurement as well as the interpolation and extrapolation, so the output is a best estimate of local activation time with an associated confidence in the estimate. This method has been extended to estimate the speed of the electrical activation wave across the left atrium in the heart, and we have also assessed how uncertainty and variability in the shape of the left atrium affects speed. We have also undertaken a systematic and detailed analysis of simulation models, so as to identify model parameters that can be identified from routinely available patient data, as well as those that cannot. This work has enabled us to develop a methodology for calibrating personalised models of individual patients, which embeds uncertainties arising from variability and noise in the data, as well as uncertainties that are associated with the methodology. This method has been evaluated using simulated patient data, and we have shown that it can be used to identify pacing protocols that optimise the information that can be obtained in the clinical setting. We have secured further EPSRC funding (EP/W000091/1) to speed up the processing pipeline so that these methods can be deployed in the clinical setting, where decisions need to be made quickly. |
| Exploitation Route | There are many ways that the outcomes of this award could be taken forward. These include the use of these techniques to guide clinical interventions, as well as direct implementation of our methods in medical equipment. We have secured further EPSRC funding to develop the technology further. |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology |
| Description | In-Procedure Personalized Atrial Digital Twin to Predict Outcome of Atrial Fibrillation Ablation |
| Amount | £1,534,181 (GBP) |
| Funding ID | EP/W000091/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2022 |
| End | 03/2025 |
| Description | The SofTMech Statistical Emulation and Translation Hub |
| Amount | £1,225,134 (GBP) |
| Funding ID | EP/T017899/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2021 |
| End | 02/2025 |
| Description | Atrial UQ |
| Organisation | King's College London |
| Department | Department of Biomedical Engineering |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Sheffield is providing expertise on uncertainty quantification. |
| Collaborator Contribution | King's College London is providing patient data and analysis expertise. |
| Impact | No outputs yet. |
| Start Year | 2017 |
| Title | quaLATi -- Quantifying Uncertainty for Local Activation Time Interpolation |
| Description | This package is for Quantifying Uncertainty for Local Activation Time Interpolation. It implements Gaussian Process Manifold Interpolation (GPMI) for doing Gaussian process regression on a manifold represented by a triangle mesh. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | This software underpins several publications including https://doi.org/10.1098/rsta.2019.0345 |
| Description | Invited presentation at the Isaac Newton Institute |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Presentation entitled "Probabilistic Calibration of Personalised Heart Models from Sparse and Noisy Measurements" invited as part of work programme on "The Role of Uncertainty in Mathematical Modelling of Pandemics" at the Isaac Newton Institute in Cambridge. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://gateway.newton.ac.uk/event/tgm110/programme |
| Description | Work Programme at the Isaac Newton Institute |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This programme took place over a month, and was based at the Isaac Newton Institute in Cambridge. It brought together a small working group of 33 attendees, who were present in Cambridge for between one week and four weeks. During the final week, there was a workshop attended by 81 participants from all over the world, and an industry day that was attended by a range of industry representatives. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.newton.ac.uk/event/fht/ |