Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.compbiomed.2019.103477 http://hdl.handle.net/11449/201208 |
Resumo: | Parkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens the life expectancy of the patients. Many studies have been dedicated to evaluating methods for early-stage PD detection, which includes machine learning techniques that employ, in most cases, motor dysfunctions, such as tremor. This work explores the time dependency in tremor signals collected from handwriting exams. To learn such temporal information, we propose a model based on Bidirectional Gated Recurrent Units along with an attention mechanism. We also introduce the concept of “Bag of Samplings” that computes multiple compact representations of the signals. Experimental results have shown the proposed model is a promising technique with results comparable to some state-of-the-art approaches in the literature. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural NetworksBag of samplingsHandwritten dynamicsParkinson's diseaseRecurrent Neural NetworksParkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens the life expectancy of the patients. Many studies have been dedicated to evaluating methods for early-stage PD detection, which includes machine learning techniques that employ, in most cases, motor dysfunctions, such as tremor. This work explores the time dependency in tremor signals collected from handwriting exams. To learn such temporal information, we propose a model based on Bidirectional Gated Recurrent Units along with an attention mechanism. We also introduce the concept of “Bag of Samplings” that computes multiple compact representations of the signals. Experimental results have shown the proposed model is a promising technique with results comparable to some state-of-the-art approaches in the literature.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)UNESP - São Paulo State University School of SciencesUFSCar - Federal University of São Carlos Department of ComputingUNESP - São Paulo State University School of SciencesFAPESP: 2013/07375-0FAPESP: 2014/12236-1CNPq: 307066/2017-7CNPq: 427968/2018-6Universidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Ribeiro, Luiz C.F. [UNESP]Afonso, Luis C.S.Papa, João P. [UNESP]2020-12-12T02:26:47Z2020-12-12T02:26:47Z2019-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.compbiomed.2019.103477Computers in Biology and Medicine, v. 115.1879-05340010-4825http://hdl.handle.net/11449/20120810.1016/j.compbiomed.2019.1034772-s2.0-85072928786Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers in Biology and Medicineinfo:eu-repo/semantics/openAccess2024-04-23T16:10:48Zoai:repositorio.unesp.br:11449/201208Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:10:48Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks |
title |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks |
spellingShingle |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks Ribeiro, Luiz C.F. [UNESP] Bag of samplings Handwritten dynamics Parkinson's disease Recurrent Neural Networks |
title_short |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks |
title_full |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks |
title_fullStr |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks |
title_full_unstemmed |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks |
title_sort |
Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks |
author |
Ribeiro, Luiz C.F. [UNESP] |
author_facet |
Ribeiro, Luiz C.F. [UNESP] Afonso, Luis C.S. Papa, João P. [UNESP] |
author_role |
author |
author2 |
Afonso, Luis C.S. Papa, João P. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de São Carlos (UFSCar) |
dc.contributor.author.fl_str_mv |
Ribeiro, Luiz C.F. [UNESP] Afonso, Luis C.S. Papa, João P. [UNESP] |
dc.subject.por.fl_str_mv |
Bag of samplings Handwritten dynamics Parkinson's disease Recurrent Neural Networks |
topic |
Bag of samplings Handwritten dynamics Parkinson's disease Recurrent Neural Networks |
description |
Parkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens the life expectancy of the patients. Many studies have been dedicated to evaluating methods for early-stage PD detection, which includes machine learning techniques that employ, in most cases, motor dysfunctions, such as tremor. This work explores the time dependency in tremor signals collected from handwriting exams. To learn such temporal information, we propose a model based on Bidirectional Gated Recurrent Units along with an attention mechanism. We also introduce the concept of “Bag of Samplings” that computes multiple compact representations of the signals. Experimental results have shown the proposed model is a promising technique with results comparable to some state-of-the-art approaches in the literature. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12-01 2020-12-12T02:26:47Z 2020-12-12T02:26:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.compbiomed.2019.103477 Computers in Biology and Medicine, v. 115. 1879-0534 0010-4825 http://hdl.handle.net/11449/201208 10.1016/j.compbiomed.2019.103477 2-s2.0-85072928786 |
url |
http://dx.doi.org/10.1016/j.compbiomed.2019.103477 http://hdl.handle.net/11449/201208 |
identifier_str_mv |
Computers in Biology and Medicine, v. 115. 1879-0534 0010-4825 10.1016/j.compbiomed.2019.103477 2-s2.0-85072928786 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computers in Biology and Medicine |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1797789785016762368 |