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On the detection of myocardial scar based on ECG/VCG analysis

On the detection of myocardial scar based on ECG/VCG analysis
On the detection of myocardial scar based on ECG/VCG analysis
In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%).
myocardial scar detection, ECG median beat, VCG, SVM, feature selection
0018-9294
3399-3409
Dima, Sofia-Maria
b3349358-a72e-4f37-bd95-eeeaeca44c5c
Panagiotou, Christos
9c789559-e749-45f9-913f-c2e736714d0e
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
Curzen, Nick
70f3ea49-51b1-418f-8e56-8210aef1abf4
Morgan, John
b9446d5b-771e-4065-a84d-d05050c7bbe4
Dima, Sofia-Maria
b3349358-a72e-4f37-bd95-eeeaeca44c5c
Panagiotou, Christos
9c789559-e749-45f9-913f-c2e736714d0e
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
Curzen, Nick
70f3ea49-51b1-418f-8e56-8210aef1abf4
Morgan, John
b9446d5b-771e-4065-a84d-d05050c7bbe4

Dima, Sofia-Maria, Panagiotou, Christos, Mazomenos, Evangelos B., Rosengarten, James, Maharatna, Koushik, Gialelis, John, Curzen, Nick and Morgan, John (2013) On the detection of myocardial scar based on ECG/VCG analysis. IEEE Transactions on Biomedical Engineering, 60 (12), 3399-3409. (doi:10.1109/TBME.2013.2279998). (PMID:24001951)

Record type: Article

Abstract

In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%).

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TBME-00875-2013_R1-PREPRINT.pdf - Accepted Manuscript
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More information

e-pub ahead of print date: 29 August 2013
Published date: December 2013
Keywords: myocardial scar detection, ECG median beat, VCG, SVM, feature selection
Organisations: Electronic & Software Systems, Human Development & Health

Identifiers

Local EPrints ID: 356968
URI: https://eprints.soton.ac.uk/id/eprint/356968
ISSN: 0018-9294
PURE UUID: dd00b94f-4672-4548-9c98-89ecb1e0e0dd

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Date deposited: 03 Oct 2013 08:53
Last modified: 19 Jul 2019 21:26

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Contributors

Author: Sofia-Maria Dima
Author: Christos Panagiotou
Author: Evangelos B. Mazomenos
Author: James Rosengarten
Author: John Gialelis
Author: Nick Curzen
Author: John Morgan

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