Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

Detalhes bibliográficos
Autor(a) principal: Abe,Jair Minoro
Data de Publicação: 2007
Outros Autores: Lopes,Helder Frederico da Silva, Anghinah,Renato
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Dementia & Neuropsychologia
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1980-57642007000300241
Resumo: Abstract EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives: To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods: Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results: The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions: We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.
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spelling Paraconsistent artificial neural networks and Alzheimer disease: A preliminary studyEEGAlzheimer diseasepattern recognitionartificial neural networkparaconsistent logic.Abstract EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives: To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods: Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results: The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions: We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.Academia Brasileira de Neurologia, Departamento de Neurologia Cognitiva e Envelhecimento2007-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1980-57642007000300241Dementia & Neuropsychologia v.1 n.3 2007reponame:Dementia & Neuropsychologiainstname:Associação de Neurologia Cognitiva e do Comportamento (ANCC)instacron:ANCC10.1590/S1980-57642008DN10300004info:eu-repo/semantics/openAccessAbe,Jair MinoroLopes,Helder Frederico da SilvaAnghinah,Renatoeng2016-09-30T00:00:00Zoai:scielo:S1980-57642007000300241Revistahttp://www.demneuropsy.com.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||demneuropsy@uol.com.br1980-57641980-5764opendoar:2016-09-30T00:00Dementia & Neuropsychologia - Associação de Neurologia Cognitiva e do Comportamento (ANCC)false
dc.title.none.fl_str_mv Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
title Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
spellingShingle Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
Abe,Jair Minoro
EEG
Alzheimer disease
pattern recognition
artificial neural network
paraconsistent logic.
title_short Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
title_full Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
title_fullStr Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
title_full_unstemmed Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
title_sort Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
author Abe,Jair Minoro
author_facet Abe,Jair Minoro
Lopes,Helder Frederico da Silva
Anghinah,Renato
author_role author
author2 Lopes,Helder Frederico da Silva
Anghinah,Renato
author2_role author
author
dc.contributor.author.fl_str_mv Abe,Jair Minoro
Lopes,Helder Frederico da Silva
Anghinah,Renato
dc.subject.por.fl_str_mv EEG
Alzheimer disease
pattern recognition
artificial neural network
paraconsistent logic.
topic EEG
Alzheimer disease
pattern recognition
artificial neural network
paraconsistent logic.
description Abstract EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives: To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods: Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results: The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions: We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.
publishDate 2007
dc.date.none.fl_str_mv 2007-09-01
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=S1980-57642007000300241
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1980-57642007000300241
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1980-57642008DN10300004
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 Academia Brasileira de Neurologia, Departamento de Neurologia Cognitiva e Envelhecimento
publisher.none.fl_str_mv Academia Brasileira de Neurologia, Departamento de Neurologia Cognitiva e Envelhecimento
dc.source.none.fl_str_mv Dementia & Neuropsychologia v.1 n.3 2007
reponame:Dementia & Neuropsychologia
instname:Associação de Neurologia Cognitiva e do Comportamento (ANCC)
instacron:ANCC
instname_str Associação de Neurologia Cognitiva e do Comportamento (ANCC)
instacron_str ANCC
institution ANCC
reponame_str Dementia & Neuropsychologia
collection Dementia & Neuropsychologia
repository.name.fl_str_mv Dementia & Neuropsychologia - Associação de Neurologia Cognitiva e do Comportamento (ANCC)
repository.mail.fl_str_mv ||demneuropsy@uol.com.br
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