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Detection of myocardial scar from the VCG using a supervised learning approach

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
b3349358-a72e-4f37-bd95-eeeaeca44c5c
Mazomenos, Evangelos B.
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Rosengarten, James
3ccf8397-ca9e-4b04-864f-5c2515db8965
Maharatna, Koushik
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Gialelis, John
a85d3764-78b8-425c-8eca-4b53a242d1c2
Morgan, John M.
ac98099e-241d-4551-bc98-709f6dfc8680
Panagiotou, Christos
9c789559-e749-45f9-913f-c2e736714d0e
Dima, Sofia-Maria
b3349358-a72e-4f37-bd95-eeeaeca44c5c
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Rosengarten, James
3ccf8397-ca9e-4b04-864f-5c2515db8965
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Gialelis, John
a85d3764-78b8-425c-8eca-4b53a242d1c2
Morgan, John M.
ac98099e-241d-4551-bc98-709f6dfc8680

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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