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

Autor(es)

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]

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

• Tipo de documento: Artigo Científico
• Fonte: FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE; v. 94, p. 282-292, MAY 2019.
• Citações Web of Science: 5

Resumo

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
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: 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
• Modalidade de apoio: Auxílio à Pesquisa - Regular