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Cited by 209Artificial Intelligence in HealthcareHandwritten Text Recognition TechniquesMotor Control and Adaptation

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

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Pereira, Clayton R., Pereira, Danilo R., Rosa, Gustavo H., Albuquerque, Victor H.C., Weber, Silke A.T., Hook, Christian, Papa, João P.·2018·Artificial Intelligence in Medicine
DOI 10.1016/j.artmed.2018.04.001
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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.

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Research works printed but not linked: 26

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Metadata

Authors
Pereira, C. R., Pereira, D. R., Rosa, G. H., Albuquerque, V. H. C., Weber, S. A. T., Hook, C., & Papa, J. P.
Affiliations
  • Universidade Federal de São Carlos (BR)
  • Universidade do Oeste Paulista (BR)
  • Universidade Estadual Paulista (Unesp) (BR)
  • OTH Regensburg (DE)
Corresponding author
Papa, João P.
ORCID
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2018-05
Volume
87
Pages
67-77
ISSN
0933-3657 (ISSN-L)
DOI
10.1016/j.artmed.2018.04.001
OpenAlex
W2800287564
Cited by
209
References
3
Citations by year
2026: 15 · 2025: 26 · 2024: 32 · 2023: 33 · 2022: 28 · 2021: 26 · 2020: 24 · 2019: 17 · 2018: 8
FWCI
23.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Artificial Intelligence in Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • Americanae (AECID Library) (submittedVersion, other-oa)
  • University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
Funders
  • Fundação de Amparo à Pesquisa do Estado de São Paulo (2013/07375-, 2014/, 307066/2017-7, 2015/, #2015/25739-4, 14/16250-9, 2013/073750, 2014/12236- 1, 16/19403-6)
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (307066/2017 — 7, 306166/2014–3, 2013/07375-0, 2014/12236-1)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
Keywords
Parkinson's disease, handwriting analysis, convolutional neural networks, computer-aided diagnosis, early detection, image-based features, texture features, motor disorders
MeSH terms
Deep Learning, Handwriting, Humans, Parkinson Disease, Neural Networks, Computer, Gait Disorders, Neurologic

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