Cargando…

Inference of ventricular activation properties from non-invasive electrocardiography

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients’ cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The inte...

Descripción completa

Detalles Bibliográficos
Autores principales: Camps, Julia, Lawson, Brodie, Drovandi, Christopher, Minchole, Ana, Wang, Zhinuo Jenny, Grau, Vicente, Burrage, Kevin, Rodriguez, Blanca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8505755/
https://www.ncbi.nlm.nih.gov/pubmed/34271532
http://dx.doi.org/10.1016/j.media.2021.102143
_version_ 1784581602563588096
author Camps, Julia
Lawson, Brodie
Drovandi, Christopher
Minchole, Ana
Wang, Zhinuo Jenny
Grau, Vicente
Burrage, Kevin
Rodriguez, Blanca
author_facet Camps, Julia
Lawson, Brodie
Drovandi, Christopher
Minchole, Ana
Wang, Zhinuo Jenny
Grau, Vicente
Burrage, Kevin
Rodriguez, Blanca
author_sort Camps, Julia
collection PubMed
description The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients’ cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac ‘digital twin’, a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes. The adoption of cardiac digital twins requires the non-invasive efficient personalisation of the electrophysiological properties in cardiac models. This study develops new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography, cardiac magnetic resonance (CMR) imaging and modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical CMR imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac ventricular myocardial-mass volumes ranging from 74 cm(3) to 171 cm(3) and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties in sinus rhythm from non-invasive epicardial activation time maps and ECG recordings, achieving higher accuracy for the endocardial speed and sheet (transmural) speed than for the fibre or sheet-normal directed speeds.
format Online
Article
Text
id pubmed-8505755
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-85057552021-10-13 Inference of ventricular activation properties from non-invasive electrocardiography Camps, Julia Lawson, Brodie Drovandi, Christopher Minchole, Ana Wang, Zhinuo Jenny Grau, Vicente Burrage, Kevin Rodriguez, Blanca Med Image Anal Article The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients’ cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac ‘digital twin’, a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes. The adoption of cardiac digital twins requires the non-invasive efficient personalisation of the electrophysiological properties in cardiac models. This study develops new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography, cardiac magnetic resonance (CMR) imaging and modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical CMR imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac ventricular myocardial-mass volumes ranging from 74 cm(3) to 171 cm(3) and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties in sinus rhythm from non-invasive epicardial activation time maps and ECG recordings, achieving higher accuracy for the endocardial speed and sheet (transmural) speed than for the fibre or sheet-normal directed speeds. Elsevier 2021-10 /pmc/articles/PMC8505755/ /pubmed/34271532 http://dx.doi.org/10.1016/j.media.2021.102143 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Camps, Julia
Lawson, Brodie
Drovandi, Christopher
Minchole, Ana
Wang, Zhinuo Jenny
Grau, Vicente
Burrage, Kevin
Rodriguez, Blanca
Inference of ventricular activation properties from non-invasive electrocardiography
title Inference of ventricular activation properties from non-invasive electrocardiography
title_full Inference of ventricular activation properties from non-invasive electrocardiography
title_fullStr Inference of ventricular activation properties from non-invasive electrocardiography
title_full_unstemmed Inference of ventricular activation properties from non-invasive electrocardiography
title_short Inference of ventricular activation properties from non-invasive electrocardiography
title_sort inference of ventricular activation properties from non-invasive electrocardiography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8505755/
https://www.ncbi.nlm.nih.gov/pubmed/34271532
http://dx.doi.org/10.1016/j.media.2021.102143
work_keys_str_mv AT campsjulia inferenceofventricularactivationpropertiesfromnoninvasiveelectrocardiography
AT lawsonbrodie inferenceofventricularactivationpropertiesfromnoninvasiveelectrocardiography
AT drovandichristopher inferenceofventricularactivationpropertiesfromnoninvasiveelectrocardiography
AT mincholeana inferenceofventricularactivationpropertiesfromnoninvasiveelectrocardiography
AT wangzhinuojenny inferenceofventricularactivationpropertiesfromnoninvasiveelectrocardiography
AT grauvicente inferenceofventricularactivationpropertiesfromnoninvasiveelectrocardiography
AT burragekevin inferenceofventricularactivationpropertiesfromnoninvasiveelectrocardiography
AT rodriguezblanca inferenceofventricularactivationpropertiesfromnoninvasiveelectrocardiography