Application of learning vector quantization for localization of myocardial infarction

Author

Reinhardt, Lutz ; Vesanto, Riikka ; Montonen, Juha ; Fetsch, Thomas ; Mäkijärvi, Markku ; Sierra, Gilberto ; Breithardt, Günter

Author_Institution

Dept. of Cardiol. & Angiol., Univ. Hospital, Munster, Germany

Volume

3

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

921

Abstract

In this study myocardial infarction was localised by a Learning Vector Quantization (LVQ) classifier. Only information about ST-elevations in all 12 leads of the standard ECG were used. The significance of proper initialisation is demonstrated. A total classification accuracy of 85.6% was achieved by a classifier trained with the optimized-learning rate LVQ1 and 50% of the 769 patients. When the classifier was further trained with the LVQ2.1 and the LVQ3 algorithms no significant improvement in the classification accuracy was observed

Keywords

electrocardiography; medical signal processing; neural nets; vector quantisation; LVQ2.1 algorithm; LVQ3 algorithm; ST-elevations; classification accuracy; electrodiagnostics; learning vector quantization; myocardial infarction localization; Artificial neural networks; Biomedical engineering; Blood; Cardiology; Electrocardiography; Hospitals; Medical tests; Myocardium; Testing; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE

Conference_Location

Amsterdam

Print_ISBN

0-7803-3811-1

Type

conf

DOI

10.1109/IEMBS.1996.652642

Filename

652642