An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters

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) d...

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Bibliographic Details
Published in:PloS one 2021-02, Vol.16 (2), p.e0244396-e0244396
Main Authors: Varrecchia, Tiwana, Castiglia, Stefano Filippo, Ranavolo, Alberto, Conte, Carmela, Tatarelli, Antonella, Coppola, Gianluca, Di Lorenzo, Cherubino, Draicchio, Francesco, Pierelli, Francesco, Serrao, Mariano
Format: Article
Language:English
Subjects:
Adaptive systems
Age
Artificial neural networks
Automation
Balance
Biology and Life Sciences
Biotechnology
Care and treatment
Classification
Complications and side effects
Computer and Information Sciences
Diagnosis
Disease
Drafting software
Editing
Environmental health
Epidemiology
Gait
Gait disorders
Gait recognition
Hygiene
Learning algorithms
Machine learning
Mathematical models
Medicine
Medicine and Health Sciences
Movement disorders
Neural networks
Neurodegenerative diseases
Parameters
Parkinson's disease
Physical Sciences
Principal components analysis
Quality of life
Rehabilitation
Reviews
Visualization
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0244396