Prediction models in the design of neural network based ECG classifiers: A neural network and genetic programming approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{nugent:2002:MIDM,
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author = "Chris D. Nugent and Jesus A. Lopez and
Ann E. Smith and Norman D. Black",
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title = "Prediction models in the design of neural network
based ECG classifiers: A neural network and genetic
programming approach",
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journal = "BMC Medical Informatics Decision Making",
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year = "2002",
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volume = "2",
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number = "1",
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pages = "1",
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month = "11 " # jan,
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keywords = "genetic algorithms, genetic programming, ANN",
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URL = "http://www.biomedcentral.com/1472-6947/2/1/abstract",
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URL = "http://www.pubmedcentral.gov/articlerender.fcgi?artid=65522",
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DOI = "doi:10.1186/1472-6947-2-1",
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size = "6 pages",
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abstract = "Background Classification of the electrocardiogram
using Neural Networks has become a widely used method
in recent years. The efficiency of these classifiers
depends upon a number of factors including network
training. Unfortunately, there is a shortage of
evidence available to enable specific design choices to
be made and as a consequence, many designs are made on
the basis of trial and error. In this study we develop
prediction models to indicate the point at which
training should stop for Neural Network based
Electrocardiogram classifiers in order to ensure
maximum generalisation. Methods Two prediction models
have been presented; one based on Neural Networks and
the other on Genetic Programming. The inputs to the
models were 5 variable training parameters and the
output indicated the point at which training should
stop. Training and testing of the models was based on
the results from 44 previously developed bi-group
Neural Network classifiers, discriminating between
Anterior Myocardial Infarction and normal patients.
Results Our results show that both approaches provide
close fits to the training data; p = 0.627 and p =
0.304 for the Neural Network and Genetic Programming
methods respectively. For unseen data, the Neural
Network exhibited no significant differences between
actual and predicted outputs (p = 0.306) while the
Genetic Programming method showed a marginally
significant difference (p = 0.047). Conclusions The
approaches provide reverse engineering solutions to the
development of Neural Network based Electrocardiogram
classifiers. That is given the network design and
architecture, an indication can be given as to when
training should stop to obtain maximum network
generalisation.",
- }
Genetic Programming entries for
Chris D Nugent
Jesus A Lopez
Ann E Smith
Norman D Black
Citations