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

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• dc.contributor.author Lampreave, Paula
• dc.contributor.author Jimenez-Perez, Guillermo
• dc.contributor.author Sanz-Pérez, Isidro
• dc.contributor.author Gomez, Alberto
• dc.contributor.author Camara, Oscar
• dc.date.accessioned 2023-03-17T07:18:48Z
• dc.date.available 2023-03-17T07:18:48Z
• dc.date.issued 2021
• dc.description.abstract 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.
• dc.description.sponsorship 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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Lampreave P, Jimenez-Perez G, Sanz I, Gomez A, Camara O. Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset. Comput Methods Biomech Biomed Engin. 2021;9(4):349-56. DOI: 10.1080/21681163.2020.1835544
• dc.identifier.doi http://dx.doi.org/10.1080/21681163.2020.1835544
• dc.identifier.issn 1025-5842
• dc.identifier.uri http://hdl.handle.net/10230/56250
• dc.language.iso eng
• dc.publisher Taylor & Francis
• dc.relation.ispartof Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2021;9(4):349-56.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/RTI2018-101193-B-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/MDM-2015-0502
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PRE2018-084062
• dc.rights © 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/10.1080/21681163.2020.1835544
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Electrocardiogram
• dc.subject.keyword medical data digitisation
• dc.subject.keyword augmented reality
• dc.subject.keyword deep learning
• dc.title Towards assisted electrocardiogram interpretation using an AI-enabled Augmented Reality headset
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion