Deep learning-aided Parkinson's disease diagnosis from handwritten dynamics
Autor(a) principal: | |
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Data de Publicação: | 2017 |
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.2016.054 http://hdl.handle.net/11449/232575 |
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 dynamicsConvolutional Neural NetworksParkinson's DiseaseParkinson'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.Department of Computing Federal University of São CarlosMedical School of Botucatu São Paulo State UniversityFakultät Informatik/Mathematik Ostbayerische Tech. HochschuleDepartment of Computing São Paulo State UniversityMedical School of Botucatu São Paulo State UniversityDepartment of Computing São Paulo State UniversityUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (UNESP)Ostbayerische Tech. HochschulePereira, Clayton R.Weber, Silke A.T. [UNESP]Hook, ChristianRosa, Gustavo H. [UNESP]Papa, Joao P. [UNESP]2022-04-29T22:42:11Z2022-04-29T22:42:11Z2017-01-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject340-346http://dx.doi.org/10.1109/SIBGRAPI.2016.054Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, p. 340-346.http://hdl.handle.net/11449/23257510.1109/SIBGRAPI.2016.0542-s2.0-85013834518Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016info:eu-repo/semantics/openAccess2022-04-29T22:42:11Zoai:repositorio.unesp.br:11449/232575Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-29T22:42:11Repositório 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. Convolutional Neural Networks Parkinson's Disease |
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 P. [UNESP] |
author_role |
author |
author2 |
Weber, Silke A.T. [UNESP] Hook, Christian Rosa, Gustavo H. [UNESP] Papa, Joao P. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (UNESP) Ostbayerische Tech. Hochschule |
dc.contributor.author.fl_str_mv |
Pereira, Clayton R. Weber, Silke A.T. [UNESP] Hook, Christian Rosa, Gustavo H. [UNESP] Papa, Joao P. [UNESP] |
dc.subject.por.fl_str_mv |
Convolutional Neural Networks Parkinson's Disease |
topic |
Convolutional Neural Networks Parkinson's Disease |
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 |
2017 |
dc.date.none.fl_str_mv |
2017-01-10 2022-04-29T22:42:11Z 2022-04-29T22:42:11Z |
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.2016.054 Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, p. 340-346. http://hdl.handle.net/11449/232575 10.1109/SIBGRAPI.2016.054 2-s2.0-85013834518 |
url |
http://dx.doi.org/10.1109/SIBGRAPI.2016.054 http://hdl.handle.net/11449/232575 |
identifier_str_mv |
Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, p. 340-346. 10.1109/SIBGRAPI.2016.054 2-s2.0-85013834518 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016 |
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.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_ |
1792961451252514816 |