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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M3C8JP
Repositorysid.inpe.br/sibgrapi/2016/07.08.22.47
Last Update2016:07.08.22.47.01 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/07.08.22.47.01
Metadata Last Update2022:06.14.00.08.18 (UTC) administrator
DOI10.1109/SIBGRAPI.2016.054
Citation KeyPereiraWebHooRosPap:2016:DeLePa
TitleDeep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics
FormatOn-line
Year2016
Access Date2024, May 01
Number of Files1
Size1328 KiB
2. Context
Author1 Pereira, Clayton Reginaldo
2 Weber, Silke Anna Theresa
3 Hook, Christian
4 Rosa, Gustavo Henrique
5 Papa, Joao Paulo
Affiliation1 Federal University of Sao Carlos
2 Sao Paulo State University
3 Ostbayerische Technische Hochschule
4 Sao Paulo State University
5 Sao Paulo State University
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addresspapa.joaopaulo@gmail.com
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherIEEE Computer Society´s Conference Publishing Services
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2016-07-08 22:47:01 :: papa.joaopaulo@gmail.com -> administrator ::
2016-10-05 14:49:09 :: administrator -> papa.joaopaulo@gmail.com :: 2016
2016-10-13 17:38:24 :: papa.joaopaulo@gmail.com -> administrator :: 2016
2022-06-14 00:08:18 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsParkinson's Disease
Convolutional Neural Networks
Deep Learning
AbstractParkinson'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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2016 > Deep Learning-aided Parkinson's...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Deep Learning-aided Parkinson's...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M3C8JP
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M3C8JP
Languageen
Target Fileopf-sibgrapi16.pdf
User Grouppapa.joaopaulo@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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