Linked Life Data

9246852

Source:http://linkedlifedata.com/resource/pubmed/id/9246852

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
1997-8-22
pubmed:abstractText
A neural network approach is proposed for the automated classification of the normal and abnormal EGG. Two learning algorithms, the quasi-Newton and the scaled conjugate gradient method for the multilayer feedforward neural networks (MFNN), are introduced and compared with the error backpropagation algorithm. The configurations of the MFNN are determined by experiment. The raw EGG data, its power spectral data, and its autoregressive moving average (ARMA) modelling parameters are used as the input to the MFNN and compared with each other. Three indexes (the percent correct, sum-squared error and complexity per iteration) are used to evaluate the performance of each learning algorithm. The results show that the scaled conjugate gradient algorithm performs best, in that it is robust and provides a super-linear convergence rate. The power spectral representation and the ARMA modelling parameters of the EGG are found to be better types of the input to the network for this specific application, both yielding a percent correctness of 95% on the test set. Although the results are focused on the classification of the EGG, this paper should provide useful information for the classification of other biomedical signals.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0140-0118
pubmed:author
pubmed:issnType
Print
pubmed:volume
35
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
199-206
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Classification of normal and abnormal electrogastrograms using multilayer feedforward neural networks.
pubmed:affiliation
University of Virginia Health Science Center, Charlottesville, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't