Application of neural classifier to risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography

Details Application of neural classifier to risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography Application of neural classifier to risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography

Jan 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008spie.6937e..86w&link_type=abstract

Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007. Edited by Romaniuk, Rys

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This paper presents the application of neural networks to the risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography. This work is based on dataset obtained from the Medical University of Warsaw. The studies were performed on one multiclass classifier and on binary classifiers. For each case the optimal number of hidden neurons was found. The effect of data preparation: normalization and the proper selection of parameters was considered, as well as the influence of applied filters. The best neural classifier contains 5 hidden neurons, the input ECG signal is represented by 8 parameters. The neural network classifier had high rate of successful recognitions up to 90% performed on the test data set.

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