Detection of myocardial scar from the VCG using a supervised learning approach
Detection of myocardial scar from the VCG using a supervised learning approach
This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the in- vestigation 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.
Panagiotou, Christos
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Dima, Sofia-Maria
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Mazomenos, Evangelos B.
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Rosengarten, James
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Maharatna, Koushik
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Gialelis, John
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Morgan, John M.
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7 July 2013
Panagiotou, Christos
9c789559-e749-45f9-913f-c2e736714d0e
Dima, Sofia-Maria
b3349358-a72e-4f37-bd95-eeeaeca44c5c
Mazomenos, Evangelos B.
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Rosengarten, James
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Maharatna, Koushik
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Gialelis, John
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Morgan, John M.
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Panagiotou, Christos, Dima, Sofia-Maria, Mazomenos, Evangelos B., Rosengarten, James, Maharatna, Koushik, Gialelis, John and Morgan, John M.
(2013)
Detection of myocardial scar from the VCG using a supervised learning approach.
35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), Japan.
03 - 07 Jul 2013.
Record type:
Conference or Workshop Item
(Other)
Abstract
This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the in- vestigation 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.
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Published date: 7 July 2013
Venue - Dates:
35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), Japan, 2013-07-03 - 2013-07-07
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 353071
URI: https://eprints.soton.ac.uk/id/eprint/353071
PURE UUID: 5b155b85-f76a-4d8c-9156-f58ed7867bf9
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Date deposited: 03 Jun 2013 08:36
Last modified: 28 Aug 2019 18:52
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Contributors
Author:
Christos Panagiotou
Author:
Sofia-Maria Dima
Author:
Evangelos B. Mazomenos
Author:
James Rosengarten
Author:
John Gialelis
Author:
John M. Morgan
University divisions
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