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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A recurrence plot-based approach for Parkinson's disease identification

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Author(s):
Afonso, Luis C. S. [1] ; Rosa, Gustavo H. [2] ; Pereira, Clayton R. [2] ; Weber, Silke A. T. [3] ; Hook, Christian [4] ; Albuquerque, Victor Hugo C. [5] ; Papa, Joao P. [2]
Total Authors: 7
Affiliation:
[1] UFSCAR Fed Univ Sao Carlos, Dept Comp, Sao Carlos, SP - Brazil
[2] UNESP Sao Paulo State Univ, Sch Sci, Bauru - Brazil
[3] UNESP Sao Paulo State Univ, Med Sch, Botucatu, SP - Brazil
[4] Ostbayer Tech Hsch, Regensburg - Germany
[5] Univ Fortaleza, Grad Program Appl Informat, Fortaleza, CE - Brazil
Total Affiliations: 5
Document type: Journal article
Source: FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE; v. 94, p. 282-292, MAY 2019.
Web of Science Citations: 5
Abstract

Parkinson's disease (PD) is a neurodegenerative disease that affects millions of people worldwide, causing mental and mainly motor dysfunctions. The negative impact on the patient's daily routine has moved the science in search of new techniques that can reduce its negative effects and also identify the disease in individuals. One of the main motor characteristics of PD is the hand tremor faced by patients, which turns out to be a crucial information to be used towards a computer-aided diagnosis. In this context, we make use of handwriting dynamics data acquired from individuals when submitted to some tasks that measure abilities related to writing skills. This work proposes the application of recurrence plots to map the signals onto the image domain, which are further used to feed a Convolutional Neural Network for learning proper information that can help the automatic identification of PD. The proposed approach was assessed in a public dataset under several scenarios that comprise different combinations of deep-based architectures, image resolutions, and training set sizes. Experimental results showed significant accuracy improvement compared to our previous work with an average accuracy of over 87%. Moreover, it was observed an improvement in accuracy concerning the classification of patients (i.e., mean recognition rates above to 90%). The promising results showed the potential of the proposed approach towards the automatic identification of Parkinson's disease. (C) 2018 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants