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CORRECTION article

Front. Physiol., 04 October 2021
Sec. Computational Physiology and Medicine
https://doi.org/10.3389/fphys.2021.765622

Corrigendum: Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

  • 1Insigneo Institute for In-Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
  • 2Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
  • 3School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
  • 4School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom

A Corrigendum on
Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

by Coveney, S., Corrado, C., Oakley, J. E., Wilkinson, R. D., Niederer, S. A., and Clayton, R. H. (2021). Front. Physiol. 12:693015. doi: 10.3389/fphys.2021.693015

In the original article, there was an omission. Equations for the posterior distribution of Restitution Curve Emulators for prediction at multiple S2 values were not provided, but these equations are required in Equation (21). Equations (18)–(20) should have been generalized from scalar S2 to vector S2.

A correction has been made to the last paragraph of Section 2. Methods, Sub-section 2.3 Restitution Curve Emulators:

Recalling Equation (6), and noting that applying a linear operation to a Gaussian process results in a Gaussian process, then the posterior distribution for the restitution curve is also a Gaussian process, which we will refer to as a Restitution Curve Emulator (RCE). Reintroducing the index c for different principal components and defining ΨC: = [Φ1(S2), …, ΦC(S2)], the RCE posterior distribution for prediction at x* for d × 1 vector S2 is given by:

(x*,S2)~N((x*,S2),V(x*,S2))    (18)
(x*,S2)=Φ0(S2)+ΨC[1(x*),,C(x*)]T    (19)
V(x*,S2)=ΨC diag[V1(x*),,VC(x*)]ΨCT    (20)

such that (x*,S2) is a d × 1 vector and V(x*,S2) is a d × d matrix. Note that the correlation between F values with similar S2 results from the principal components (S2 does not index the random variables). RCEs are built for ERP(S1) restitution curves in exactly the same way as for APD(S2) and CV(S2) restitution curves. Prediction with RCEs is orders of magnitude faster than simulation, with ~104 predictions taking only a few seconds on a laptop (i5 gen 6 processor, 8 Gb RAM).

In the original article, there was an omission. Equation (21) was missing an identity matrix factor.

A correction has been made to Section 2. Methods, Subsection 2.5 Calibration, Equation 21:

      Y|(x,S2Y)~N((x,S2Y),σY2I)Y~N((x,S2Y),V(x,S2Y)+σY2I)    (21)

Figure Correction

In the original article, there was a mistake in Figures 813 as published. The computer code for the likelihood function for CV(S2) and APD(S2), used for our MCMC simulations, only accounted for the diagonal of the posterior variance matrix V(x,S2Y). The corrected Figures 813 shown here.

FIGURE 8
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Figure 8. The RCE prediction from maximum a posteriori (MAP) parameter estimates given noisy measurements for (left) CV(S2) and ERP(S1), (right) APD(S2) and ERP(S1), shown as light shaded regions representing RCE 95% confidence intervals. The orange dashed curves show these intervals including the observation error, also learned from MAP fitting. The noisy S2 restitution data are shown as crosses, while the red shaded bars represent observed intervals containing ERP: (top): bars horizontally span ERP(S1:600) interval; (bottom) bars vertically span ERP(S1) interval for several S1. The solid black lines in all plots represent the corresponding ground truth curves.

FIGURE 9
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Figure 9. RCE predictions, shown as lightly shaded regions representing 95% confidence intervals, for 100 parameter samples from the posterior distribution given the same measurements shown in Figure 8 [black crosses are noisy S2 restitution data, red bars are observed ERP intervals, (left) MCMC with CV(S2) and ERP(S1) data, (right) MCMC with APD(S2) and ERP(S1) data].

FIGURE 10
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Figure 10. The posterior parameter distribution for fits to CV(S2) and ERP(S1) measurements. The intersection of vertical and horizontal lines mark the true parameter value. The lower diagonal shows the density via hexbin plots, while the upper diagonal shows the log likelihood values for each sample plotted in order of increasing likelihood. The diagonals show the marginal histograms of each parameter.

FIGURE 11
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Figure 11. The posterior parameter distribution for fits to APD(S2) and ERP(S1) measurements. The intersection of vertical and horizontal lines mark the true parameter value. The lower diagonal shows the density via hexbin plots, while the upper diagonal shows the log-likelihood values for each sample plotted in order of increasing likelihood. The diagonals show the marginal histograms of each parameter.

FIGURE 12
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Figure 12. RCE predictions, shown as lightly shaded regions representing 95% confidence intervals, for 100 parameter samples from the posterior distribution given the same measurements shown in Figure 8 (black crosses are noisy S2 restitution data, red bars are observed ERP intervals). MCMC utilized CV(S2), APD(S2), and ERP(S1) data simultaneously, unlike in Figures 8, 9.

FIGURE 13
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Figure 13. The posterior parameter distribution for calibration to CV(S2), APD(S2), and ERP(S1) measurements simultaneously. The intersection of vertical and horizontal lines mark the true parameter value. The lower diagonal shows the density via hexbin plots, while the upper diagonal shows the log likelihood values for each sample plotted in order of increasing likelihood. The diagonals show the marginal histograms of each parameter.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: restitution, electrophysiology, cardiology, Gaussian processes, emulation, sensitivity analysis, calibration, Bayesian

Citation: Coveney S, Corrado C, Oakley JE, Wilkinson RD, Niederer SA and Clayton RH (2021) Corrigendum: Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators. Front. Physiol. 12:765622. doi: 10.3389/fphys.2021.765622

Received: 27 August 2021; Accepted: 07 September 2021;
Published: 04 October 2021.

Edited and reviewed by: Linwei Wang, Rochester Institute of Technology, United States

Copyright © 2021 Coveney, Corrado, Oakley, Wilkinson, Niederer and Clayton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sam Coveney, coveney.sam@gmail.com

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