CORRECTION article

Front. Physiol., 04 October 2021

Sec. Computational Physiology and Medicine

Volume 12 - 2021 | https://doi.org/10.3389/fphys.2021.765622

Corrigendum: Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators

  • 1. Insigneo Institute for In-Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom

  • 2. Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom

  • 3. School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom

  • 4. School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom

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:

such that is a d × 1 vector and is a d × d matrix. Note that the correlation between 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:

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 . The corrected Figures 813 shown here.

Figure 8

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Figure 13

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.

Summary

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

Volume

12 - 2021

Edited and reviewed by

Linwei Wang, Rochester Institute of Technology, United States

Updates

Copyright

*Correspondence: Sam Coveney

This article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology

Disclaimer

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.

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