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Author de Chazal, P. ♦ Celler, B.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1997
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science ♦ Technology ♦ Medicine & health
Subject Keyword Electrocardiography ♦ Diseases ♦ Databases ♦ Testing ♦ Myocardium ♦ Area measurement ♦ Discrete wavelet transforms ♦ Laboratories ♦ Neural networks ♦ Artificial neural networks
Abstract The authors investigated the problem of selecting parameters for inclusion in neural networks for diagnostic classification of the Frank lead ECG as normal or one of six disease conditions. A database of 486 100% accurate classified cases was randomly divided into a training set (67%) and a test set (33%). Using a total of 274 parameters as well as the age and sex data the authors determined the discriminating power of each parameter with receiver operator characteristic analysis as well as the rank correlation of all possible parameter pairs. On the basis of the discriminating power and rank correlation, a number of different parameter selection schemes were considered. The authors achieved best classification rates on the test data set by selecting parameters which were maximally discriminating and non correlated.
Description Author affiliation: Biomed. Syst. Lab., New South Wales Univ., Sydney, NSW, Australia (de Chazal, P.)
ISBN 0780344456
ISSN 02766547
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1997-09-07
Publisher Place Sweden
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 436.46 kB
Page Count 4
Starting Page 13
Ending Page 16


Source: IEEE Xplore Digital Library