Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram
Autor(a) principal: | |
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Data de Publicação: | 2009 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Medical and Biological Research |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2009001200014 |
Resumo: | The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of the classifier led to a linear model. A dimension reduction that resulted in only 25% of uncorrelated electrogastrogram characteristics gave 24 inputs for the classifier. The prandial stage gave the most significant results. Under these conditions, the classifier achieved 78.6% sensitivity, 92.9% specificity, and an error of 17.9 ± 6% (with a 95% confidence level). These data show that it is possible to establish significant differences between patients and normal controls when time-frequency characteristics are extracted from an electrogastrogram, with an adequate component reduction, outperforming the results obtained with classical Fourier analysis. These findings can contribute to increasing our understanding of the pathophysiological mechanisms involved in functional dyspepsia and perhaps to improving the pharmacological treatment of functional dyspeptic patients. |
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Brazilian Journal of Medical and Biological Research |
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Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogramFunctional dyspepsiaElectrogastrographyWavelet transformNeural networksThe aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of the classifier led to a linear model. A dimension reduction that resulted in only 25% of uncorrelated electrogastrogram characteristics gave 24 inputs for the classifier. The prandial stage gave the most significant results. Under these conditions, the classifier achieved 78.6% sensitivity, 92.9% specificity, and an error of 17.9 ± 6% (with a 95% confidence level). These data show that it is possible to establish significant differences between patients and normal controls when time-frequency characteristics are extracted from an electrogastrogram, with an adequate component reduction, outperforming the results obtained with classical Fourier analysis. These findings can contribute to increasing our understanding of the pathophysiological mechanisms involved in functional dyspepsia and perhaps to improving the pharmacological treatment of functional dyspeptic patients.Associação Brasileira de Divulgação Científica2009-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2009001200014Brazilian Journal of Medical and Biological Research v.42 n.12 2009reponame:Brazilian Journal of Medical and Biological Researchinstname:Associação Brasileira de Divulgação Científica (ABDC)instacron:ABDC10.1590/S0100-879X2009007500012info:eu-repo/semantics/openAccessChacón,M.Curilem,G.Acuña,G.Defilippi,C.Madrid,A.M.Jara,S.eng2009-12-04T00:00:00Zoai:scielo:S0100-879X2009001200014Revistahttps://www.bjournal.org/https://old.scielo.br/oai/scielo-oai.phpbjournal@terra.com.br||bjournal@terra.com.br1414-431X0100-879Xopendoar:2009-12-04T00:00Brazilian Journal of Medical and Biological Research - Associação Brasileira de Divulgação Científica (ABDC)false |
dc.title.none.fl_str_mv |
Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram |
title |
Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram |
spellingShingle |
Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram Chacón,M. Functional dyspepsia Electrogastrography Wavelet transform Neural networks |
title_short |
Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram |
title_full |
Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram |
title_fullStr |
Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram |
title_full_unstemmed |
Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram |
title_sort |
Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram |
author |
Chacón,M. |
author_facet |
Chacón,M. Curilem,G. Acuña,G. Defilippi,C. Madrid,A.M. Jara,S. |
author_role |
author |
author2 |
Curilem,G. Acuña,G. Defilippi,C. Madrid,A.M. Jara,S. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Chacón,M. Curilem,G. Acuña,G. Defilippi,C. Madrid,A.M. Jara,S. |
dc.subject.por.fl_str_mv |
Functional dyspepsia Electrogastrography Wavelet transform Neural networks |
topic |
Functional dyspepsia Electrogastrography Wavelet transform Neural networks |
description |
The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of the classifier led to a linear model. A dimension reduction that resulted in only 25% of uncorrelated electrogastrogram characteristics gave 24 inputs for the classifier. The prandial stage gave the most significant results. Under these conditions, the classifier achieved 78.6% sensitivity, 92.9% specificity, and an error of 17.9 ± 6% (with a 95% confidence level). These data show that it is possible to establish significant differences between patients and normal controls when time-frequency characteristics are extracted from an electrogastrogram, with an adequate component reduction, outperforming the results obtained with classical Fourier analysis. These findings can contribute to increasing our understanding of the pathophysiological mechanisms involved in functional dyspepsia and perhaps to improving the pharmacological treatment of functional dyspeptic patients. |
publishDate |
2009 |
dc.date.none.fl_str_mv |
2009-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2009001200014 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2009001200014 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0100-879X2009007500012 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Divulgação Científica |
publisher.none.fl_str_mv |
Associação Brasileira de Divulgação Científica |
dc.source.none.fl_str_mv |
Brazilian Journal of Medical and Biological Research v.42 n.12 2009 reponame:Brazilian Journal of Medical and Biological Research instname:Associação Brasileira de Divulgação Científica (ABDC) instacron:ABDC |
instname_str |
Associação Brasileira de Divulgação Científica (ABDC) |
instacron_str |
ABDC |
institution |
ABDC |
reponame_str |
Brazilian Journal of Medical and Biological Research |
collection |
Brazilian Journal of Medical and Biological Research |
repository.name.fl_str_mv |
Brazilian Journal of Medical and Biological Research - Associação Brasileira de Divulgação Científica (ABDC) |
repository.mail.fl_str_mv |
bjournal@terra.com.br||bjournal@terra.com.br |
_version_ |
1754302937778094080 |