Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/67258

TitleArtificial neural networks classification of patients with parkinsonism based on gait
Author(s)Fernandes, Carlos
Fonseca, Luis
Ferreira, Flora
Gago, Miguel
Costa, Luís
Sousa, Nuno
Ferreira, Carlos
Gama, João
Erlhagen, Wolfram
Bicho, Estela
KeywordsMultiple Layer Perceptrons
Multiple Layer Perceptrons
Belief Net-works
Idiopathic Parkinson's disease
Vascular Parkinsonism
Walking
Deep Belief Networks
Issue date2018
PublisherIEEE
JournalIEEE International Conference on Bioinformatics and Biomedicine - BIBM
CitationFernandes, C., Fonseca, L., Ferreira, F., Gago, M., Costa, L., Sousa, N., ... & Bicho, E. (2018, December). Artificial neural networks classification of patients with parkinsonism based on gait. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2024-2030). IEEE
Abstract(s)Differential diagnosis between Idiopathic Parkin-son's disease (IPD) and Vascular Parkinsonism (VaP) is a difficult task, especially early in the disease. There is growing evidence to support the use of gait assessment in diagnosis and management of movement disorder diseases. The aim of this study is to evaluate the effectiveness of some machine learning strategies in distinguishing IPD and VaP gait. Wearable sensors positioned on both feet were used to acquire the gait data from 15 IPD, 15 VaP, and 15 healthy subjects. A comparative classification analysis was performed by applying two supervised machine learning algorithms: Multiple Layer Perceptrons (MLPs) and Deep Belief Networks (DBNs). The decisional space was composed of the gait variables, with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top-ranked in an error incremental analysis. In the classification task of characterizing parkinsonian gait by distinguishing between patients (IPD+VaP) and healthy control, from the all strides classification of the gait performed by the person, high accuracy (93% with or without MoCA) was obtained for both algorithms. In the classification task of the two groups of patients (VaP vs. IPD), DBN classifier achieved higher performance (73% with MoCA). To the best of our knowledge, this is the first study on gait classification that includes a VaP group. DBN classifiers are not frequently applied in literature to similar studies, but the results here obtained demonstrate that the use of DBN classifiers based on gait analysis is promising to be a good support to the neurologist in distinguishing VaP and IPD.
TypeConference paper
URIhttps://hdl.handle.net/1822/67258
ISBN978-1-5386-5487-3
e-ISBN978-1-5386-5488-0
DOI10.1109/BIBM.2018.8621466
ISSN2156-1125
Publisher versionhttps://ieeexplore.ieee.org/abstract/document/8621466
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
ICVS - Artigos em livros de atas / Papers in proceedings

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