@InProceedings{PereiraWebHooRosPap:2016:DeLePa,
               author = "Pereira, Clayton Reginaldo and Weber, Silke Anna Theresa and Hook, 
                         Christian and Rosa, Gustavo Henrique and Papa, Joao Paulo",
          affiliation = "{Federal University of Sao Carlos} and {Sao Paulo State 
                         University} and {Ostbayerische Technische Hochschule} and {Sao 
                         Paulo State University} and {Sao Paulo State University}",
                title = "Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten 
                         Dynamics",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             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.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.054",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.054",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M3C8JP",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M3C8JP",
           targetfile = "opf-sibgrapi16.pdf",
        urlaccessdate = "2024, May 02"
}