Utilising a Genetic Algorithm to Minimise the Number of Leads in Body Surface Mapping for the Electrocardiographic Diagnosis of Myocardial Infarction

PJ Scott, CO Navarro, M Giardina, OJ Escalona, JMCC Anderson, AAJ Adgey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The 80-lead Body Surface Map (BSM) is a diagnostic tool utilised by clinicians for the diagnosis of myocardial infarction (MI) at our centre. The optimum number andplacement of leads on the BSM is uncertain. We used Genetic Algorithm (GA) analysis to determine a reduced lead system for the optimal diagnosis of MI. 1106 casespresenting to our centre with ischaemic type chest pain (576 ST Segment Elevation MI, 244 Atypical ECG and 286 Non-MI) were recorded using the 80-lead BSM. A GA was developed to determine a subset of reduced number of leads, with their associated anatomicalposition within the 80-lead BSM system, while maintaining sensitivity and specificity for MI diagnosis. The GA was run on two separate occasions (Run A and Run B) and the output compared with the 80-Lead BSM. Run A produced a 24 lead system. The sensitivity and specificity for MI diagnosis was 86.40% and 97.55% respectively. Received Operator Characteristic (ROC) curve c-statistic was 0.805. Run B produced a 21 lead system with sensitivity and specificity of 84.84% and 98.25% respectively. ROC curve c-statistic was 0.811. This compares favourably with the 80 lead BSM (sensitivity 90%, specificity 92%, ROC c-statistic 0.850).
LanguageEnglish
Title of host publicationUnknown Host Publication
Place of Publicationwww
Pages297-300
Number of pages4
Volume37
Publication statusPublished - 15 Nov 2010
EventComputing in Cardiology - Belfast-UK
Duration: 15 Nov 2010 → …
http://www.cinc.org

Conference

ConferenceComputing in Cardiology
Period15/11/10 → …
Internet address

Fingerprint

Body Surface Potential Mapping
Myocardial Infarction
Sensitivity and Specificity
Lead
Chest Pain
Infarction
Electrocardiography

Keywords

  • Body Surface Cardiac Mapping
  • Genetic Algorithm
  • Data Mining
  • ECG
  • Myocardial Infarction Detection

Cite this

Scott, PJ., Navarro, CO., Giardina, M., Escalona, OJ., Anderson, JMCC., & Adgey, AAJ. (2010). Utilising a Genetic Algorithm to Minimise the Number of Leads in Body Surface Mapping for the Electrocardiographic Diagnosis of Myocardial Infarction. In Unknown Host Publication (Vol. 37, pp. 297-300). www.
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abstract = "The 80-lead Body Surface Map (BSM) is a diagnostic tool utilised by clinicians for the diagnosis of myocardial infarction (MI) at our centre. The optimum number andplacement of leads on the BSM is uncertain. We used Genetic Algorithm (GA) analysis to determine a reduced lead system for the optimal diagnosis of MI. 1106 casespresenting to our centre with ischaemic type chest pain (576 ST Segment Elevation MI, 244 Atypical ECG and 286 Non-MI) were recorded using the 80-lead BSM. A GA was developed to determine a subset of reduced number of leads, with their associated anatomicalposition within the 80-lead BSM system, while maintaining sensitivity and specificity for MI diagnosis. The GA was run on two separate occasions (Run A and Run B) and the output compared with the 80-Lead BSM. Run A produced a 24 lead system. The sensitivity and specificity for MI diagnosis was 86.40{\%} and 97.55{\%} respectively. Received Operator Characteristic (ROC) curve c-statistic was 0.805. Run B produced a 21 lead system with sensitivity and specificity of 84.84{\%} and 98.25{\%} respectively. ROC curve c-statistic was 0.811. This compares favourably with the 80 lead BSM (sensitivity 90{\%}, specificity 92{\%}, ROC c-statistic 0.850).",
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author = "PJ Scott and CO Navarro and M Giardina and OJ Escalona and JMCC Anderson and AAJ Adgey",
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Scott, PJ, Navarro, CO, Giardina, M, Escalona, OJ, Anderson, JMCC & Adgey, AAJ 2010, Utilising a Genetic Algorithm to Minimise the Number of Leads in Body Surface Mapping for the Electrocardiographic Diagnosis of Myocardial Infarction. in Unknown Host Publication. vol. 37, www, pp. 297-300, Computing in Cardiology, 15/11/10.

Utilising a Genetic Algorithm to Minimise the Number of Leads in Body Surface Mapping for the Electrocardiographic Diagnosis of Myocardial Infarction. / Scott, PJ; Navarro, CO; Giardina, M; Escalona, OJ; Anderson, JMCC; Adgey, AAJ.

Unknown Host Publication. Vol. 37 www, 2010. p. 297-300.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Utilising a Genetic Algorithm to Minimise the Number of Leads in Body Surface Mapping for the Electrocardiographic Diagnosis of Myocardial Infarction

AU - Scott, PJ

AU - Navarro, CO

AU - Giardina, M

AU - Escalona, OJ

AU - Anderson, JMCC

AU - Adgey, AAJ

PY - 2010/11/15

Y1 - 2010/11/15

N2 - The 80-lead Body Surface Map (BSM) is a diagnostic tool utilised by clinicians for the diagnosis of myocardial infarction (MI) at our centre. The optimum number andplacement of leads on the BSM is uncertain. We used Genetic Algorithm (GA) analysis to determine a reduced lead system for the optimal diagnosis of MI. 1106 casespresenting to our centre with ischaemic type chest pain (576 ST Segment Elevation MI, 244 Atypical ECG and 286 Non-MI) were recorded using the 80-lead BSM. A GA was developed to determine a subset of reduced number of leads, with their associated anatomicalposition within the 80-lead BSM system, while maintaining sensitivity and specificity for MI diagnosis. The GA was run on two separate occasions (Run A and Run B) and the output compared with the 80-Lead BSM. Run A produced a 24 lead system. The sensitivity and specificity for MI diagnosis was 86.40% and 97.55% respectively. Received Operator Characteristic (ROC) curve c-statistic was 0.805. Run B produced a 21 lead system with sensitivity and specificity of 84.84% and 98.25% respectively. ROC curve c-statistic was 0.811. This compares favourably with the 80 lead BSM (sensitivity 90%, specificity 92%, ROC c-statistic 0.850).

AB - The 80-lead Body Surface Map (BSM) is a diagnostic tool utilised by clinicians for the diagnosis of myocardial infarction (MI) at our centre. The optimum number andplacement of leads on the BSM is uncertain. We used Genetic Algorithm (GA) analysis to determine a reduced lead system for the optimal diagnosis of MI. 1106 casespresenting to our centre with ischaemic type chest pain (576 ST Segment Elevation MI, 244 Atypical ECG and 286 Non-MI) were recorded using the 80-lead BSM. A GA was developed to determine a subset of reduced number of leads, with their associated anatomicalposition within the 80-lead BSM system, while maintaining sensitivity and specificity for MI diagnosis. The GA was run on two separate occasions (Run A and Run B) and the output compared with the 80-Lead BSM. Run A produced a 24 lead system. The sensitivity and specificity for MI diagnosis was 86.40% and 97.55% respectively. Received Operator Characteristic (ROC) curve c-statistic was 0.805. Run B produced a 21 lead system with sensitivity and specificity of 84.84% and 98.25% respectively. ROC curve c-statistic was 0.811. This compares favourably with the 80 lead BSM (sensitivity 90%, specificity 92%, ROC c-statistic 0.850).

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KW - Genetic Algorithm

KW - Data Mining

KW - ECG

KW - Myocardial Infarction Detection

M3 - Conference contribution

VL - 37

SP - 297

EP - 300

BT - Unknown Host Publication

CY - www

ER -