Herrero-Martín, C.; Oved, A.; Chowdhury, RA.; Ullmann, E.; Peters, NS.; Bharath, AA.; Varela, M. (2022). EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks. Frontiers in Cardiovascular Medicine. 8:1-15. https://doi.org/10.3389/fcvm.2021.768419
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/194698
Título:
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EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks
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Autor:
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Herrero-Martín, Clara
Oved, Alon
Chowdhury, Rasheda A.
Ullmann, Elisabeth
Peters, Nicholas S.
Bharath, Anil A.
Varela, Marta
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Fecha difusión:
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Resumen:
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[EN] Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation. ...[+]
[EN] Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation. It is, however, notoriously difficult to perform. We present EP-PINNs (Physics Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation from sparse amounts of EP data. We demonstrate, using 1D and 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporal evolution of action potentials, whilst predicting parameters related to action potential duration (APD), excitability and diffusion coefficients. EP-PINNs are additionally able to identify heterogeneities in EP properties, making them potentially useful for the detection of fibrosis and other localised pathology linked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological in vitro preparations, by characterising the effect of anti-arrhythmic drugs on APD using optical mapping data. EP-PINNs are a promising clinical tool for the characterisation and potential treatment guidance of arrhythmias.
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Palabras clave:
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Cardiac electrophysiology
,
Arrhythmia (any)
,
Physics Informed Neural Network (PINN)
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Atrial fibrillation
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Parameter estimation
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Optical mapping
,
Biophysical modelling
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Artificial intelligence
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Derechos de uso:
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Reconocimiento (by)
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Fuente:
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Frontiers in Cardiovascular Medicine. (eissn:
2297-055X
)
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DOI:
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10.3389/fcvm.2021.768419
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Editorial:
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Frontiers Media SA
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Versión del editor:
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https://doi.org/10.3389/fcvm.2021.768419
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Código del Proyecto:
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info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//ACIF%2F2021%2F205//APLICACIÓN DE LA INTELIGENCIA ARTIFICIAL AL GUIADO DE LA ABLACIÓN DE ARRITMIAS CARDIACAS MEDIANTE MAPEO GLOBAL/
info:eu-repo/grantAgreement/BHF//RE%2F18%2F4%2F34215/
info:eu-repo/grantAgreement/BHF//RG%2F16%2F3%2F32175/
info:eu-repo/grantAgreement/BHF//PG%2F16%2F17%2F32069/
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Agradecimientos:
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This work was supported by the British Heart Foundation
(RE/18/4/34215, RG/16/3/32175, PG/16/17/32069, and Centre
of Research Excellence), the Imperial-TUM Seed Funding, the
National Institute for Health Research (UK) ...[+]
This work was supported by the British Heart Foundation
(RE/18/4/34215, RG/16/3/32175, PG/16/17/32069, and Centre
of Research Excellence), the Imperial-TUM Seed Funding, the
National Institute for Health Research (UK) Biomedical Research
Centre and the Rosetrees Trust through the interdisciplinary
award Atrial Fibrillation: A Major Clinical Challenge and
the Generalitat Valenciana Conselleria d'Educació, Investigació,
Cultura i Esport (ACIF/2021/205).
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Tipo:
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Artículo
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