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Intelligent Data Analysis for the Classification of Body Surface Potential Maps

Donnelly, Mark, Nugent, Christopher, Finlay, Dewar and Black, Norman (2008) Intelligent Data Analysis for the Classification of Body Surface Potential Maps. International Journal of Computational Intelligence and Applications, 7 (3). pp. 249-263. [Journal article]

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URL: http://www.worldscinet.com/ijcia/07/0703/S1469026808002284.html

DOI: 10.1142/S1469026808002284


Body surface potential maps were investigated to identify a set of optimal recording sites required to discriminate between several diseases. Specifically, recordings captured from subjects exhibiting myocardial infarction or left ventricular hypertrophy, as well as a control group consisting of healthy subjects were investigated. Due to the fact that multi-class problems are inherently difficult to solve we divided the problem into several two-class scenarios. Six datasets were generated from the available 744 records, each viewing the available data differently, to form several two-class problems. A data-driven selection algorithm was applied to each of the generated datasets to produce six classification models, each utilising as features those recording sites offering most to the discrimination task being investigated. Subsequently, a framework was introduced to facilitate the combination of outputs from each classifier. Essentially, the framework used the outputs from half of the classification models to determine which of the remaining models would be employed to form a final decision. A benchmark, in the form of a multi-group classifier, was introduced to evaluate the perceived benefits of the proposed approach. An improvement of approximately 10% upon the benchmark was observed resulting in an overall accuracy of 79.19%.

Item Type:Journal article
Keywords:Body surface potential mapping; feature subset selection; wrapper; limited lead sets; diagnostic ECG
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:364
Deposited By: Dr Mark Donnelly
Deposited On:27 Oct 2009 14:52
Last Modified:22 Jul 2011 10:43

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