Application of learning vector quantization for localization of myocardial infarction
- Resource Type
- Conference
- Authors
- Reinhardt, L.; Vesanto, R.; Montonen, J.; Fetsch, T.; Makijarvi, M.; Sierra, G.; Breithardt, G.
- Source
- Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Engineering in medicine and biology Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE. 3:921-922 vol.3 1996
- Subject
- Bioengineering
Vector quantization
Myocardium
Electrocardiography
Testing
Medical tests
Artificial neural networks
Cardiology
Hospitals
Biomedical engineering
Blood
- Language
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.