This paper analyses a strategy for ischemia detection based on wavelet decomposition of the ST segment. The wavelet transform is used as a pre-processing tool for linear discriminant classifier. In order to minimize generalization problems caused by correlations between the classification variables, a selection algorithm is employed to choose a subset of wavelet coefficients with appropriate discriminability and small collinearity. When applied to a set with small morfologic variability, good results are obtained: 98.5% of accuracy and a ROC Area equal to 0.98. However, when the training set has a high within-class scatter, the discriminant model yields poor results. © 2005 IEEE.
CITATION STYLE
Sales, F. J. R., Jayanthi, S., Furuie, S. S., & Galvão, R. K. H. (2005). An ischemia detector based on wavelet analysis of electrocardiogram ST segments. In Computers in Cardiology (Vol. 32, pp. 865–868). IEEE Computer Society. https://doi.org/10.1109/CIC.2005.1588242
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