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Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space

Panagiotis Barmpoutis; Kosmas Dimitropoulos; Anestis Apostolidis; Nikos Grammalidis


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  <identifier identifierType="DOI">10.5281/zenodo.3678671</identifier>
  <creators>
    <creator>
      <creatorName>Panagiotis Barmpoutis</creatorName>
      <affiliation>Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London</affiliation>
    </creator>
    <creator>
      <creatorName>Kosmas Dimitropoulos</creatorName>
      <affiliation>Visual Computing Lab, Information Technologies Institute, Center for Research and Technology Hellas,</affiliation>
    </creator>
    <creator>
      <creatorName>Anestis Apostolidis</creatorName>
      <affiliation>Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University,</affiliation>
    </creator>
    <creator>
      <creatorName>Nikos Grammalidis</creatorName>
      <affiliation>Visual Computing Lab, Information Technologies Institute, Center for Research and Technology Hellas,</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-02-21</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3678671</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3678670</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Electrocardiogram is commonly used as a diagnostic tool for the monitoring of cardiac health and the detection of possible heart diseases. However, the procedure followed for the diagnosis of heart abnormalities is time consuming and prone to human errors. Thus, the development of computer-aided techniques for the automatic analysis of electrocardiogram signals is of vital importance for the diagnosis and prevention of heart diseases. The most serious outcome of coronary heart disease is the myocardial infarction, i.e. the rapid and irreversible damage of cardiac muscles, which, if not diagnosed and treated in time, continues to damage further the myocardial structure and function. In this paper we propose a novel approach for the automatic detection and localization of myocardial infarction from multi-lead electrocardiogram signals.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/690494/">690494</awardNumber>
      <awardTitle>Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS</awardTitle>
    </fundingReference>
  </fundingReferences>
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