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		<doi>10.1109/SIBGRAPI.2016.054</doi>
		<citationkey>PereiraWebHooRosPap:2016:DeLePa</citationkey>
		<title>Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics</title>
		<format>On-line</format>
		<year>2016</year>
		<numberoffiles>1</numberoffiles>
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		<author>Pereira, Clayton Reginaldo,</author>
		<author>Weber, Silke Anna Theresa,</author>
		<author>Hook, Christian,</author>
		<author>Rosa, Gustavo Henrique,</author>
		<author>Papa, Joao Paulo,</author>
		<affiliation>Federal University of Sao Carlos</affiliation>
		<affiliation>Sao Paulo State University</affiliation>
		<affiliation>Ostbayerische Technische Hochschule</affiliation>
		<affiliation>Sao Paulo State University</affiliation>
		<affiliation>Sao Paulo State University</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>papa.joaopaulo@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>IEEE Computer Society´s Conference Publishing Services</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>Parkinson's Disease, Convolutional Neural Networks, Deep Learning.</keywords>
		<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.</abstract>
		<language>en</language>
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		<usergroup>papa.joaopaulo@gmail.com</usergroup>
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