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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Scott, PJ, Navarro, CO, Giardina, M, Escalona, OJ, Anderson, JMCC and 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: Computing in Cardiology, Belfast-UK. IEEE. Vol 37 4 pp. [Conference contribution]

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URL: http://www.cinc.org/archives/2010/

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).

Item Type:Conference contribution (Paper)
Keywords:Body Surface Cardiac Mapping Genetic Algorithm Data Mining ECG Myocardial Infarction Detection
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Engineering
Research Institutes and Groups:Engineering Research Institute
Engineering Research Institute > Nanotechnology & Integrated BioEngineering Centre (NIBEC)
ID Code:17359
Deposited By:Professor Omar Escalona
Deposited On:09 May 2012 13:25
Last Modified:09 May 2012 13:25

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