Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators

Coveney, Sam; Clayton, Richard H.

doi:10.3389/fphys.2020.00364

Cited by 14 publications

(14 citation statements)

References 53 publications

(83 reference statements)

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“…Gaussian process (GP) emulators, which provide a prediction and corresponding prediction uncertainty, can be effective emulators of complex computer models (Conti and O'Hagan, 2010). GP emulators have been used for sensitivity analysis (Chang et al, 2015;Coveney and Clayton, 2020) and history matching (Coveney and Clayton, 2018) of cardiac cell models, and for models of cardiac tissue (Dhamala et al, 2020;Lawson et al, 2020) and mechanics (Longobardi et al, 2020). Emulators are conditioned on precalculated simulator data, but since they can make predictions at new inputs they are ideal tools for MCMC.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

et al. 2021

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Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel “restitution curve emulators” as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data. These emulators are built by decomposing restitution curves using principal component analysis and modeling the resulting coordinates with respect to model parameters using Gaussian processes. Restitution curve emulators can be used to study parameter identifiability via sensitivity analysis of restitution curve components and rapid inference of the posterior distribution of model parameters given noisy measurements. Posterior uncertainty about parameters is critical for making predictions from calibrated models, since many parameter settings can be consistent with measured data and yet produce very different model behaviors under conditions not effectively probed by the measurement protocols. Restitution curve emulators are therefore promising probabilistic tools for calibrating electrophysiology models.

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Section: Introductionmentioning

confidence: 99%

Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

et al. 2021

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“… 24 This amounted to the analysis of 136 parameters. Using the Latin Hypercube method 10 , 24 we sampled 1100 points from the 136-dimensional input parameter space by varying the variables within ±40% of their nominal value. These 1100 points were split into a training dataset of 900 points and a validation dataset of 200 points.…”

Section: Methodsmentioning

confidence: 99%

“…The number of training points was chosen after performing a convergence study to guarantee a sufficient quality of the emulator fit on the validation dataset. Discrepancies between simulator and emulator were assessed through the mean average percentage error (MAPE), 10 defined as where and are the results from the -th run of the simulator and emulator respectively, and . is the mean of the simulator output.…”

Section: Methodsmentioning

confidence: 99%

“…The quality of the emulator fit was deemed acceptable when the MAPE was <7% on the entire validation dataset, in alignment with previous studies. 10 …”

Section: Methodsmentioning

confidence: 99%

“…The second aim is the identification of biomarkers for distal perfusion that could be computed from TCD measures routinely performed in a clinical setting. We employ Gaussian process emulators for the analysis of arterial blood velocities, 24 and Sobol sensitivity analysis 10 , 24 for the identification of biomarkers. This process recognises the potential variability of anatomical parameters within the patient population, and the effects of this on results variability.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach

Benemerito

Narata²,

Narracott

et al. 2022

Ann Biomed Eng

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Assessment of distal cerebral perfusion after ischaemic stroke is currently only possible through expensive and time-consuming imaging procedures which require the injection of a contrast medium. Alternative approaches that could indicate earlier the impact of blood flow occlusion on distal cerebral perfusion are currently lacking. The aim of this study was to identify novel biomarkers suitable for clinical implementation using less invasive diagnostic techniques such as Transcranial Doppler (TCD). We used 1D modelling to simulate pre- and post-stroke velocity and flow wave propagation in a typical arterial network, and Sobol’s sensitivity analysis, supported by the use of Gaussian process emulators, to identify biomarkers linked to cerebral perfusion. We showed that values of pulsatility index of the right anterior cerebral artery > 1.6 are associated with poor perfusion and may require immediate intervention. Three additional biomarkers with similar behaviour, all related to pulsatility indices, were identified. These results suggest that flow pulsatility measured at specific locations could be used to effectively estimate distal cerebral perfusion rates, and ultimately improve clinical diagnosis and management of ischaemic stroke.

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Assessing input parameter hyperspace and parameter identifiability in a cardiovascular system model via sensitivity analysis

Saxton,

Xu,

Schenkel

et al. 2024

Journal of Computational Science

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Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators

Cited by 14 publications

References 53 publications

Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach

Assessing input parameter hyperspace and parameter identifiability in a cardiovascular system model via sensitivity analysis

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