Utilising surrogate models to approximate cardiac potentials when solving inverse problems via Bayesian techniques

Abbish Kamalakkannan*, Peter Johnston, Barbara Johnston

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


Solving inverse problems is computationally expensive, if not infeasible, under specific scenarios. For example, many forward solutions are required when solving inverse problems using Bayesian techniques. In this work, a novel inference protocol is established, that can be used to infer the cardiac bidomain conductivities and the cardiac fibre rotation angle (bidomain parameters). This protocol uses a surrogate model, developed using generalised polyno- mial chaos techniques, to approximate cardiac potentials on a multi-electrode array. The resulting surrogate model is used in conjunction with Bayesian inference techniques to infer the bidomain parameters. A lower-order surrogate model (order three) can effectively characterise the influ- ence of the extracellular conductivities and fibre rotation on the cardiac potentials; however, it is recommended that a higher-order surrogate model expansion of order seven be used to adequately characterise the influence of the in- tracellular conductivities as well. This seventh order sur- rogate model was successfully used to infer the extracellu- lar conductivities and fibre rotation angle from a single set of synthetically generated noisy experimental potentials, while the intracellular conductivities were unable to be re- trieved accurately under this scenario.
Original languageEnglish
Title of host publicationComputing in Cardiology Proceedings 2022
Place of PublicationFinland
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9798350300970
Publication statusPublished - 2022
Externally publishedYes
EventComputing in Cardiology 2022, CinC 2022 - Tampere, Finland
Duration: 4 Sept 20227 Sept 2022

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


ConferenceComputing in Cardiology 2022, CinC 2022


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