Automated Classification of Postural Control for Individuals With Parkinson’s Disease Using a Machine Learning Approach: A Preliminary Study

Author ORCID Identifier

Christina Odeh:https://orcid.org/0000-0002-5966-9329

Publication Title

Journal of Applied Biomechanics

ISSN

10658483

E-ISSN

44043

Document Type

Article

Abstract

The purposes of the study were (1) to compare postural sway between participants with Parkinson's disease (PD) and healthy controls and (2) to develop and validate an automated classification of PD postural control patterns using a machine learning approach. A total of 9 participants in the early stage of PD and 12 healthy controls were recruited. Participants were instructed to stand on a force plate and maintain stillness for 2 minutes with eyes open and eyes closed. The center of pressure data were collected at 50 Hz. Linear displacements, standard deviations, total distances, sway areas, and multiscale entropy of center of pressure were calculated and compared using mixed-model analysis of variance. Five supervised machine learning algorithms (ie, logistic regression, K-nearest neighbors, Naïve Bayes, decision trees, and random forest) were used to classify PD postural control patterns. Participants with PD exhibited greater center of pressure sway and variability compared with controls. The K-nearest neighbormethod exhibited the best prediction performance with an accuracy rate of up to 0.86. In conclusion, participants with PD exhibited impaired postural stability and their postural sway features could be identified by machine learning algorithms.

First Page

334

Last Page

339

Publication Date

10-1-2020

DOI

10.1123/JAB.2019-0400

PubMed ID

32736341

Keywords

Balance, Center of pressure, Elderly, Machine learning classifier, Postural stability

Department

School of Allied Health and Communicative Disorders

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