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Publication Detail
Detection of myocardial scar from the VCG using a supervised learning approach
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Panagiotou C, Dima SM, Mazomenos EB, Rosengarten J, Maharatna K, Gialelis J, Morgan J
  • Publication date:
    31/10/2013
  • Pagination:
    7326, 7329
  • Journal:
    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
  • Status:
    Published
  • Print ISSN:
    1557-170X
Abstract
This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue. © 2013 IEEE.
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