Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study
Autor(a) principal: | |
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Data de Publicação: | 2007 |
Outros Autores: | , |
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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Dementia & Neuropsychologia |
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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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1754212929203339264 |