Neural networks for ECG classification

The performance of the neural network approach in the diagnostic classification of 12-lead electrocardiograms (ECG) is investigated. For this study a validated ECG database established at the University of Leuven is used. Previous results obtained from the same database to derive two classifiers based on statistical models (linear discriminant analysis and logistic discriminant analysis) are taken as reference points in the evaluation. A simple neural network architecture is chosen: the feed-forward structure with the use of the back-propagation algorithm. Sensitivity, specificity, total and partial accuracy are the indices used for the assessment of the performance. The results show a comparable behavior with the two statistical methods.<<ETX>>

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