Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/50344

TitleApplication of machine learning in postural control kinematics for the diagnosis of Alzheimer’s disease
Author(s)Costa, Luís
Gago, Miguel F.
Yelshyna, Darya
Ferreira, Jaime
Silva, Hélder David Malheiro
Rocha, Luís Alexandre Machado
Sousa, Nuno
Bicho, Estela
Issue date9-Jan-2016
PublisherHindawi Publishing Corporation
JournalComputational Intelligence and Neuroscience
CitationCosta, L., Gago, M. F., Yelshyna, D., Ferreira, J., David Silva, H., Rocha, L., ... & Bicho, E. (2016). Application of machine learning in postural control kinematics for the diagnosis of Alzheimer’s disease. Computational intelligence and neuroscience, 2016
Abstract(s)The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support VectorMachines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.
TypeArticle
URIhttp://hdl.handle.net/1822/50344
DOI10.1155/2016/3891253
ISSN1687-5265
Publisher versionhttps://www.hindawi.com/journals/cin/2016/3891253/abs/
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:DEI - Artigos em revistas internacionais
ICVS - Artigos em Revistas Internacionais com Referee

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