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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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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]
Número total de Autores: 7
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 5
Tipo de documento: Artigo Científico
Citações Web of Science: 3

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)

Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
Beneficiário:Alexandre Xavier Falcão
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
Beneficiário:José Alberto Cuminato
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
Beneficiário:João Paulo Papa
Linha de fomento: Auxílio à Pesquisa - Regular