Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

Detalhes bibliográficos
Autor(a) principal: Chacón,M.
Data de Publicação: 2009
Outros Autores: Curilem,G., Acuña,G., Defilippi,C., Madrid,A.M., Jara,S.
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.
id ABDC-1_a7b70e147408b49574a09372aedb89f2
oai_identifier_str oai:scielo:S0100-879X2009001200014
network_acronym_str ABDC-1
network_name_str Brazilian Journal of Medical and Biological Research
repository_id_str
spelling 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