Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/SIBGRAPI.2016.51 http://hdl.handle.net/11449/163002 |
Resumo: | Parkinson's Disease (PD) automatic identification in early stages is one of the most challenging medicine-related tasks to date, since a patient may have a similar behaviour to that of a healthy individual at the very early stage of the disease. In this work, we cope with PD automatic identification by means of a Convolutional Neural Network (CNN), which aims at learning features from a signal extracted during the individual's exam by means of a smart pen composed of a series of sensors that can extract information from handwritten dynamics. We have shown CNNs are able to learn relevant information, thus outperforming results obtained from raw data. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster PD-related research. |
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Repositório Institucional da UNESP |
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Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten DynamicsParkinson's DiseaseConvolutional Neural NetworksDeep LearningParkinson's Disease (PD) automatic identification in early stages is one of the most challenging medicine-related tasks to date, since a patient may have a similar behaviour to that of a healthy individual at the very early stage of the disease. In this work, we cope with PD automatic identification by means of a Convolutional Neural Network (CNN), which aims at learning features from a signal extracted during the individual's exam by means of a smart pen composed of a series of sensors that can extract information from handwritten dynamics. We have shown CNNs are able to learn relevant information, thus outperforming results obtained from raw data. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster PD-related research.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed Sao Carlos, Dept Comp, Sao Carlos, SP, BrazilSao Paulo State Univ, Med Sch Botucatu, Botucatu, SP, BrazilOstbayer Tech Hsch, Fak Informat Math, Regensburg, GermanySao Paulo State Univ, Dept Comp, Bauru, SP, BrazilSao Paulo State Univ, Med Sch Botucatu, Botucatu, SP, BrazilSao Paulo State Univ, Dept Comp, Bauru, SP, BrazilFAPESP: 2010/15566-1FAPESP: 2014/16250-9FAPESP: 2015/25739-4CNPq: 306166/2014-3IeeeUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Ostbayer Tech HschPereira, Clayton R.Weber, Silke A. T. [UNESP]Hook, ChristianRosa, Gustavo H. [UNESP]Papa, Joao [UNESP]IEEE2018-11-26T17:39:43Z2018-11-26T17:39:43Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject340-346http://dx.doi.org/10.1109/SIBGRAPI.2016.512016 29th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 340-346, 2016.1530-1834http://hdl.handle.net/11449/16300210.1109/SIBGRAPI.2016.51WOS:000405493800045Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2016 29th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi)info:eu-repo/semantics/openAccess2021-10-23T21:44:19Zoai:repositorio.unesp.br:11449/163002Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:19Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics |
title |
Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics |
spellingShingle |
Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics Pereira, Clayton R. Parkinson's Disease Convolutional Neural Networks Deep Learning |
title_short |
Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics |
title_full |
Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics |
title_fullStr |
Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics |
title_full_unstemmed |
Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics |
title_sort |
Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics |
author |
Pereira, Clayton R. |
author_facet |
Pereira, Clayton R. Weber, Silke A. T. [UNESP] Hook, Christian Rosa, Gustavo H. [UNESP] Papa, Joao [UNESP] IEEE |
author_role |
author |
author2 |
Weber, Silke A. T. [UNESP] Hook, Christian Rosa, Gustavo H. [UNESP] Papa, Joao [UNESP] IEEE |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) Ostbayer Tech Hsch |
dc.contributor.author.fl_str_mv |
Pereira, Clayton R. Weber, Silke A. T. [UNESP] Hook, Christian Rosa, Gustavo H. [UNESP] Papa, Joao [UNESP] IEEE |
dc.subject.por.fl_str_mv |
Parkinson's Disease Convolutional Neural Networks Deep Learning |
topic |
Parkinson's Disease Convolutional Neural Networks Deep Learning |
description |
Parkinson's Disease (PD) automatic identification in early stages is one of the most challenging medicine-related tasks to date, since a patient may have a similar behaviour to that of a healthy individual at the very early stage of the disease. In this work, we cope with PD automatic identification by means of a Convolutional Neural Network (CNN), which aims at learning features from a signal extracted during the individual's exam by means of a smart pen composed of a series of sensors that can extract information from handwritten dynamics. We have shown CNNs are able to learn relevant information, thus outperforming results obtained from raw data. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster PD-related research. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 2018-11-26T17:39:43Z 2018-11-26T17:39:43Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/SIBGRAPI.2016.51 2016 29th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 340-346, 2016. 1530-1834 http://hdl.handle.net/11449/163002 10.1109/SIBGRAPI.2016.51 WOS:000405493800045 |
url |
http://dx.doi.org/10.1109/SIBGRAPI.2016.51 http://hdl.handle.net/11449/163002 |
identifier_str_mv |
2016 29th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi). New York: Ieee, p. 340-346, 2016. 1530-1834 10.1109/SIBGRAPI.2016.51 WOS:000405493800045 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2016 29th Sibgrapi Conference On Graphics, Patterns And Images (sibgrapi) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
340-346 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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 |
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1792961728791707648 |