An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2021
2021
Historique:
received:
17
06
2020
accepted:
08
12
2020
entrez:
19
2
2021
pubmed:
20
2
2021
medline:
20
7
2021
Statut:
epublish
Résumé
Gait deficits are debilitating in people with Parkinson's disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease. Our study aimed to i) develop an automated diagnostic algorithm based on machine-learning techniques (artificial neural networks [ANNs]) to classify the gait deficits of PwPD according to disease progression in the Hoehn and Yahr (H-Y) staging system, and ii) identify a minimum set of gait classifiers. We evaluated 76 PwPD (H-Y stage 1-4) and 67 healthy controls (HCs) by computerized gait analysis. We computed the time-distance parameters and the ranges of angular motion (RoMs) of the hip, knee, ankle, trunk, and pelvis. Principal component analysis was used to define a subset of features including all gait variables. An ANN approach was used to identify gait deficits according to the H-Y stage. We identified a combination of a small number of features that distinguished PwPDs from HCs (one combination of two features: knee and trunk rotation RoMs) and identified the gait patterns between different H-Y stages (two combinations of four features: walking speed and hip, knee, and ankle RoMs; walking speed and hip, knee, and trunk rotation RoMs). The ANN approach enabled automated diagnosis of gait deficits in several symptomatic stages of Parkinson's disease. These results will inspire future studies to test the utility of gait classifiers for the evaluation of treatments that could modify disease progression.
Identifiants
pubmed: 33606730
doi: 10.1371/journal.pone.0244396
pii: PONE-D-20-18587
pmc: PMC7894951
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0244396Déclaration de conflit d'intérêts
NO authors have competing interests.
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