@InProceedings{AfonsoPerWebHooPap:2017:PaDiId,
author = "Afonso, Luis Claudio Sugi and Pereira, Clayton Reginaldo and
Weber, Silke Anna Theresa and Hook, Christian and Papa, Jo{\~a}o
Paulo",
affiliation = "UFSCar - Federal University of S{\~a}o Carlos, Department of
Computing, S{\~a}o Carlos, Brazil and UFSCar - Federal University
of S{\~a}o Carlos, Department of Computing, S{\~a}o Carlos,
Brazil and UNESP - S{\~a}o Paulo State University, Medical
School, Botucatu, Brazil and Ostbayerische Tech. Hochschule,
Fakultat Informatik/Mathematik, Regensburg, Germany and UNESP -
S{\~a}o Paulo State University, School of Sciences, Bauru,
Brazil",
title = "Parkinson's Disease Identification Through Deep Optimum-Path
Forest Clustering",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Parkinson's disease, Optimum-Path Forest, Handwriting Dynamics.",
abstract = "Approximately 50,000 to 60,000 new cases of Parkinson's disease
(PD) are diagnosed yearly. Despite being non-lethal, PD shortens
life expectancy of the ones affected with such disease. As such,
researchers from different fields of study have put great effort
in order to develop methods aiming the identification of PD in its
early stages. This work uses handwriting dynamics data acquired by
a series of tasks and proposes the application of a deep-driven
graph-based clustering algorithm known as Optimum-Path Forest to
learn a dictionary-like representation of each individual in order
to automatic identify Parkinson's disease. Experimental results
have shown promising results, with results comparable to some
state-of-the-art approaches in the literature.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.28",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.28",
language = "en",
ibi = "8JMKD3MGPAW/3PF999L",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF999L",
targetfile = "PID4953679.pdf",
urlaccessdate = "2024, May 02"
}