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Reduction of invasive tests in chagasic patients with a modified self-organizing map

Marmol-Herrera, L. and Warwick, K. (2001) Reduction of invasive tests in chagasic patients with a modified self-organizing map. In: 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 1676-1679. ISBN 0780372115

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To link to this item DOI: 10.1109/IEMBS.2001.1020537


The applicability of AI methods to the Chagas' disease diagnosis is carried out by the use of Kohonen's self-organizing feature maps. Electrodiagnosis indicators calculated from ECG records are used as features in input vectors to train the network. Cross-validation results are used to modify the maps, providing an outstanding improvement to the interpretation of the resulting output. As a result, the map might be used to reduce the need for invasive explorations in chronic Chagas' disease.

Item Type:Book or Report Section
ID Code:21615
Uncontrolled Keywords:AI methods, Chagas disease diagnosis, ECG features, Kohonen self-organizing feature maps, cross-validation, electrodiagnosis indicators, information cost function, input vectors, invasive tests reduction, low frequency features, myocardial damage, signal classification, training parameters, wavelet decomposition

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