Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset

The interpretation of electrocardiograms (ECGs) is key for the diagnosis and monitoring of cardiovascular health. Despite the progressive digital transformation in healthcare, it is still common for clinicians to analyse ECG printed on paper. Although some systems provide signal processing-based ECG...

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Detalhes bibliográficos
Autores: Lampreave, Paula, Jimenez-Perez, Guillermo, Sanz-Pérez, Isidro, Gomez, Alberto, Camara, Oscar
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Recursos:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/56250
Acesso em linha:http://hdl.handle.net/10230/56250
http://dx.doi.org/10.1080/21681163.2020.1835544
Access Level:acceso abierto
Palavra-chave:Electrocardiogram
medical data digitisation
augmented reality
deep learning
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spelling Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headsetLampreave, PaulaJimenez-Perez, GuillermoSanz-Pérez, IsidroGomez, AlbertoCamara, OscarElectrocardiogrammedical data digitisationaugmented realitydeep learningThe interpretation of electrocardiograms (ECGs) is key for the diagnosis and monitoring of cardiovascular health. Despite the progressive digital transformation in healthcare, it is still common for clinicians to analyse ECG printed on paper. Although some systems provide signal processing-based ECG classification, clinicians often find it unreliable. Artificial Intelligence (AI) techniques are becoming state-of-the-art for ECG processing but the lack of digitised ECG has hampered the clinical translation of these techniques. Concurrently, we are living a rise in augmented reality (AR) technologies, with an increasing availability of devices. In this work, we present an automatic digitisation and assisted interpretation of ECG based on an AI-enabled Augmented Reality headset. The AR headset is used to acquire an image of the printed ECG, from which the digitised ECG signal is extracted. Afterwards, the digitised ECG is introduced into a Deep Learning (DL) algorithm pre-trained on a public database of 12-lead ECG recordings. The output of the DL algorithm classifies the ECG signal onto different cardiomyopathy categories, which is then visualized back in the AR headset. Preliminary classification results on simulated ECG images (96.5% of accuracy) confirm the potential of the developed approach to contribute on the digital transformation of ECG processing.This work was supported by the Ministerio de Ciencia, Innovación y Universidades under the Retos I+D Programme (RTI2018-101193-B-I00), the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and the Ministerio de Economíay Competitividad under the Programme for the Formation of Doctors (PRE2018-084062). Alberto Gomez acknowl-edges financial support from the Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy’s and St Thomas’ NHS Foundation Trust in partnership with King's College London and King’s College Hospital NHS Foundation Trust.Taylor & Francis202320232021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/56250http://dx.doi.org/10.1080/21681163.2020.1835544reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2021;9(4):349-56.info:eu-repo/grantAgreement/ES/2PE/RTI2018-101193-B-I00info:eu-repo/grantAgreement/ES/1PE/MDM-2015-0502info:eu-repo/grantAgreement/ES/2PE/PRE2018-084062© This is an Accepted Manuscript of an article published by Taylor & Francis in COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING on 27-10-2020, available online: http://www.tandfonline.com/10.1080/21681163.2020.1835544info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/562502026-06-12T07:21:37Z
dc.title.none.fl_str_mv Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
title Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
spellingShingle Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
Lampreave, Paula
Electrocardiogram
medical data digitisation
augmented reality
deep learning
title_short Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
title_full Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
title_fullStr Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
title_full_unstemmed Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
title_sort Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
dc.creator.none.fl_str_mv Lampreave, Paula
Jimenez-Perez, Guillermo
Sanz-Pérez, Isidro
Gomez, Alberto
Camara, Oscar
author Lampreave, Paula
author_facet Lampreave, Paula
Jimenez-Perez, Guillermo
Sanz-Pérez, Isidro
Gomez, Alberto
Camara, Oscar
author_role author
author2 Jimenez-Perez, Guillermo
Sanz-Pérez, Isidro
Gomez, Alberto
Camara, Oscar
author2_role author
author
author
author
dc.subject.none.fl_str_mv Electrocardiogram
medical data digitisation
augmented reality
deep learning
topic Electrocardiogram
medical data digitisation
augmented reality
deep learning
description The interpretation of electrocardiograms (ECGs) is key for the diagnosis and monitoring of cardiovascular health. Despite the progressive digital transformation in healthcare, it is still common for clinicians to analyse ECG printed on paper. Although some systems provide signal processing-based ECG classification, clinicians often find it unreliable. Artificial Intelligence (AI) techniques are becoming state-of-the-art for ECG processing but the lack of digitised ECG has hampered the clinical translation of these techniques. Concurrently, we are living a rise in augmented reality (AR) technologies, with an increasing availability of devices. In this work, we present an automatic digitisation and assisted interpretation of ECG based on an AI-enabled Augmented Reality headset. The AR headset is used to acquire an image of the printed ECG, from which the digitised ECG signal is extracted. Afterwards, the digitised ECG is introduced into a Deep Learning (DL) algorithm pre-trained on a public database of 12-lead ECG recordings. The output of the DL algorithm classifies the ECG signal onto different cardiomyopathy categories, which is then visualized back in the AR headset. Preliminary classification results on simulated ECG images (96.5% of accuracy) confirm the potential of the developed approach to contribute on the digital transformation of ECG processing.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/56250
http://dx.doi.org/10.1080/21681163.2020.1835544
url http://hdl.handle.net/10230/56250
http://dx.doi.org/10.1080/21681163.2020.1835544
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2021;9(4):349-56.
info:eu-repo/grantAgreement/ES/2PE/RTI2018-101193-B-I00
info:eu-repo/grantAgreement/ES/1PE/MDM-2015-0502
info:eu-repo/grantAgreement/ES/2PE/PRE2018-084062
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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