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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.)

Handwritten dynamics dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification

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Author(s):
Pereira, Clayton R. [1] ; Pereira, Danilo R. [2] ; Rosa, Gustavo H. [3] ; Albuquerque, Victor H. C. [4] ; Weber, Silke A. T. [5] ; Hook, Christian [6] ; Papa, Joao P. [3]
Total Authors: 7
Affiliation:
[1] UFSCAR Fed Univ Sao Carlos, Dept Comp, Sao Carlos, SP - Brazil
[2] UNOESTE Univ Western Sao Paulo, Presidente Prudente - Brazil
[3] UNESP Sao Paulo State Univ, Sch Sci, Bauru - Brazil
[4] UNIFOR Grad Program Appl Informat, Fortaleza, Ceara - Brazil
[5] UNESP Sao Paulo State Univ, Botucatu Med Sch, Botucatu, SP - Brazil
[6] OTH, Regensburg - Germany
Total Affiliations: 6
Document type: Journal article
Source: ARTIFICIAL INTELLIGENCE IN MEDICINE; v. 87, p. 67-77, MAY 2018.
Web of Science Citations: 15
Abstract

Background and objective: Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. Methods: In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual's assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. Results: The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. Conclusions: The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson's disease identification, as well as it can outperform handcrafted features. (C) 2018 Elsevier B.V. All rights reserved. (AU)

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: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 10/15566-1 - Evaluation of mathematic modell for study of fine motor function in individuals with Parkinson's disease using a multi-sensor biometric smart pen, BiSP
Grantee:Silke Anna Theresa Weber
Support Opportunities: Regular Research Grants
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: 15/25739-4 - On the Study of Semantics in Deep Learning Models
Grantee:Gustavo Henrique de Rosa
Support Opportunities: Scholarships in Brazil - Master