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Title: Comparison of the classification ability of the electrocardiogram and vectorcardiogram
Authors: Willems, J L ×
Lesaffre, Emmanuel
Pardaens, J #
Issue Date: Jan-1987
Series Title: The American journal of cardiology vol:59 issue:1 pages:119-24
Abstract: Controversy exists over the classification ability of the standard 12-lead electrocardiogram (EGG) and the vectorcardiogram (VCG). In this study the diagnostic information content and classification performance of the ECG and VCG were examined using multivariate statistical techniques and a large validated data base of 3,266 cases. Logistic classification models were developed to differentiate between 7 diagnostic entities: normal (n = 538), left (n = 557), right (n = 323) and biventricular (n = 437) hypertrophy, and anterior (n = 390), inferior (n = 657) and combined (n = 364) myocardial infarction. The models were obtained from a learning sample (n = 2,446) using an optimal set of computer derived ECG and VCG measurements. They were subsequently applied to a test sample (n = 820). In the learning sample, the discrimination models resulted in a total correct classification rate of 69.6% for the ECG and 69.4% for the VCG. The total accuracy rate was slightly lower in the test set: 66.3% for the ECG and 67.1% for the VCG. The combined use of the best ECG and VCG variables did not increase total diagnostic accuracy. When cases with biventricular hypertrophy and combined infarction were deleted, accuracy rates of more than 80% were achieved for both lead systems. Differences in the classification rates for the subgroups were not statistically significant. Thus, the conventional 12-lead ECG is as good as the VCG for the differential diagnosis of 7 main entities, provided identical procedures are used in the design of the classifiers.(ABSTRACT TRUNCATED AT 250 WORDS)
URI: 
ISSN: 0002-9149
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat)
× corresponding author
# (joint) last author

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