Quantifying and Visualizing Uncertainty for Source Localisation in Electrocardiographic Imaging

Dennis K. Njeru; Tushar M. Athawale; Jessie J. France; Chris R. Johnson

doi:10.1080/21681163.2022.2113824

Quantifying and Visualizing Uncertainty for Source Localisation in Electrocardiographic Imaging

Dennis K. Njeru, Tushar M. Athawale, Jessie J. France, Chris R. Johnson

Visualization

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualise associated measurement and modelling errors. In this paper, we study source localisation uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localisation model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualisation techniques, including confidence maps, level-sets, and topology-based visualisations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline.

Original language	English
Pages (from-to)	812-822
Number of pages	11
Journal	Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Volume	11
Issue number	3
DOIs	https://doi.org/10.1080/21681163.2022.2113824
State	Published - 2023

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, world-wide licence to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). Acknowledgments This project was supported by grants from the National Institute of General Medical Sciences (P41 GM103545-18, R24 GM136986) and the Intel Graphics and Visualization Institutes of XeLLENCE, the Scientific Discovery through Advanced Computing (SciDAC) program in the U.S. Department of Energy. The authors would like to give special thanks to Professor Rob MacLeod for generously sharing the electrocardiogram data. The authors would like to give special thanks to Professor Rob MacLeod for generously sharing the electrocardiogram data.

Keywords

Electrocardiographic imaging (ECGI)
Monte Carlo simulation
uncertainty visualisation

Access to Document

10.1080/21681163.2022.2113824

Cite this

@article{54eabd2467914b849d54626856f81424,

title = "Quantifying and Visualizing Uncertainty for Source Localisation in Electrocardiographic Imaging",

abstract = "Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualise associated measurement and modelling errors. In this paper, we study source localisation uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localisation model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualisation techniques, including confidence maps, level-sets, and topology-based visualisations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline.",

keywords = "Electrocardiographic imaging (ECGI), Monte Carlo simulation, uncertainty visualisation",

author = "Njeru, {Dennis K.} and Athawale, {Tushar M.} and France, {Jessie J.} and Johnson, {Chris R.}",

note = "Publisher Copyright: {\textcopyright} 2022 Informa UK Limited, trading as Taylor & Francis Group.",

year = "2023",

doi = "10.1080/21681163.2022.2113824",

language = "English",

volume = "11",

pages = "812--822",

journal = "Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization",

issn = "2168-1163",

publisher = "Taylor and Francis Group",

number = "3",

}

TY - JOUR

T1 - Quantifying and Visualizing Uncertainty for Source Localisation in Electrocardiographic Imaging

AU - Njeru, Dennis K.

AU - Athawale, Tushar M.

AU - France, Jessie J.

AU - Johnson, Chris R.

PY - 2023

Y1 - 2023

N2 - Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualise associated measurement and modelling errors. In this paper, we study source localisation uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localisation model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualisation techniques, including confidence maps, level-sets, and topology-based visualisations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline.

AB - Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualise associated measurement and modelling errors. In this paper, we study source localisation uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localisation model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualisation techniques, including confidence maps, level-sets, and topology-based visualisations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline.

KW - Electrocardiographic imaging (ECGI)

KW - Monte Carlo simulation

KW - uncertainty visualisation

UR - http://www.scopus.com/inward/record.url?scp=85138368747&partnerID=8YFLogxK

U2 - 10.1080/21681163.2022.2113824

DO - 10.1080/21681163.2022.2113824

M3 - Article

AN - SCOPUS:85138368747

SN - 2168-1163

VL - 11

SP - 812

EP - 822

JO - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

JF - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization

IS - 3

ER -

Quantifying and Visualizing Uncertainty for Source Localisation in Electrocardiographic Imaging

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this