%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.08.22.47
%2 sid.inpe.br/sibgrapi/2016/07.08.22.47.01
%@doi 10.1109/SIBGRAPI.2016.054
%T Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics
%D 2016
%A Pereira, Clayton Reginaldo,
%A Weber, Silke Anna Theresa,
%A Hook, Christian,
%A Rosa, Gustavo Henrique,
%A Papa, Joao Paulo,
%@affiliation Federal University of Sao Carlos
%@affiliation Sao Paulo State University
%@affiliation Ostbayerische Technische Hochschule
%@affiliation Sao Paulo State University
%@affiliation Sao Paulo State University
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K Parkinson's Disease, Convolutional Neural Networks, Deep Learning.
%X 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.
%@language en
%3 opf-sibgrapi16.pdf