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Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers

This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (H...

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Detalles Bibliográficos
Autores principales: Fraiwan, Luay, Hassanin, Omnia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177554/
https://www.ncbi.nlm.nih.gov/pubmed/34086723
http://dx.doi.org/10.1371/journal.pone.0252380
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author Fraiwan, Luay
Hassanin, Omnia
author_facet Fraiwan, Luay
Hassanin, Omnia
author_sort Fraiwan, Luay
collection PubMed
description This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on statistical amplitude quantification using the root mean square (RMS), variance, kurtosis, and skewness metrics. We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of multi-class classification. Experimental results showed that AdaBoost ensembling provided a 6.49%, 0.78%, 2.31%, and 2.72% prediction rate improvement for the VGRF, stride, stance, and swing signals, respectively. The proposed approach achieved the highest classification accuracy of 99.17%, sensitivity of 98.23%, and specificity of 99.43%, using the VGRF-based features and the adaptive boosting classification model. This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs. Computer-aided analysis of gait fluctuations provides a promising advent to enhance clinical diagnosis of DNDs.
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spelling pubmed-81775542021-06-07 Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers Fraiwan, Luay Hassanin, Omnia PLoS One Research Article This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on statistical amplitude quantification using the root mean square (RMS), variance, kurtosis, and skewness metrics. We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of multi-class classification. Experimental results showed that AdaBoost ensembling provided a 6.49%, 0.78%, 2.31%, and 2.72% prediction rate improvement for the VGRF, stride, stance, and swing signals, respectively. The proposed approach achieved the highest classification accuracy of 99.17%, sensitivity of 98.23%, and specificity of 99.43%, using the VGRF-based features and the adaptive boosting classification model. This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs. Computer-aided analysis of gait fluctuations provides a promising advent to enhance clinical diagnosis of DNDs. Public Library of Science 2021-06-04 /pmc/articles/PMC8177554/ /pubmed/34086723 http://dx.doi.org/10.1371/journal.pone.0252380 Text en © 2021 Fraiwan, Hassanin https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Fraiwan, Luay
Hassanin, Omnia
Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers
title Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers
title_full Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers
title_fullStr Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers
title_full_unstemmed Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers
title_short Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers
title_sort computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8177554/
https://www.ncbi.nlm.nih.gov/pubmed/34086723
http://dx.doi.org/10.1371/journal.pone.0252380
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