1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPAW/3M3C8JP |
Repository | sid.inpe.br/sibgrapi/2016/07.08.22.47 |
Last Update | 2016:07.08.22.47.01 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2016/07.08.22.47.01 |
Metadata Last Update | 2022:06.14.00.08.18 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2016.054 |
Citation Key | PereiraWebHooRosPap:2016:DeLePa |
Title | Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics |
Format | On-line |
Year | 2016 |
Access Date | 2024, May 01 |
Number of Files | 1 |
Size | 1328 KiB |
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2. Context | |
Author | 1 Pereira, Clayton Reginaldo 2 Weber, Silke Anna Theresa 3 Hook, Christian 4 Rosa, Gustavo Henrique 5 Papa, Joao Paulo |
Affiliation | 1 Federal University of Sao Carlos 2 Sao Paulo State University 3 Ostbayerische Technische Hochschule 4 Sao Paulo State University 5 Sao Paulo State University |
Editor | Aliaga, 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 Address | papa.joaopaulo@gmail.com |
Conference Name | Conference on Graphics, Patterns and Images, 29 (SIBGRAPI) |
Conference Location | São José dos Campos, SP, Brazil |
Date | 4-7 Oct. 2016 |
Publisher | IEEE Computer Society´s Conference Publishing Services |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full 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 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | Parkinson's Disease Convolutional Neural Networks Deep Learning |
Abstract | 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. |
Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2016 > Deep Learning-aided Parkinson's... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Deep Learning-aided Parkinson's... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPAW/3M3C8JP |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3M3C8JP |
Language | en |
Target File | opf-sibgrapi16.pdf |
User Group | papa.joaopaulo@gmail.com |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3M2D4LP 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2016/07.02.23.50 5 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy 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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