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Stable Sparse Classifiers predict cognitive impairment from gait patterns

BACKGROUND: Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose. PU...

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Autores principales: Aznielle-Rodríguez, Tania, Ontivero-Ortega, Marlis, Galán-García, Lídice, Sahli, Hichem, Valdés-Sosa, Mitchell
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425080/
https://www.ncbi.nlm.nih.gov/pubmed/36051195
http://dx.doi.org/10.3389/fpsyg.2022.894576
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author Aznielle-Rodríguez, Tania
Ontivero-Ortega, Marlis
Galán-García, Lídice
Sahli, Hichem
Valdés-Sosa, Mitchell
author_facet Aznielle-Rodríguez, Tania
Ontivero-Ortega, Marlis
Galán-García, Lídice
Sahli, Hichem
Valdés-Sosa, Mitchell
author_sort Aznielle-Rodríguez, Tania
collection PubMed
description BACKGROUND: Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose. PURPOSES: To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC. METHODS: A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified. RESULTS: The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers. CONCLUSIONS: The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.
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spelling pubmed-94250802022-08-31 Stable Sparse Classifiers predict cognitive impairment from gait patterns Aznielle-Rodríguez, Tania Ontivero-Ortega, Marlis Galán-García, Lídice Sahli, Hichem Valdés-Sosa, Mitchell Front Psychol Psychology BACKGROUND: Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose. PURPOSES: To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC. METHODS: A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified. RESULTS: The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers. CONCLUSIONS: The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques. Frontiers Media S.A. 2022-08-16 /pmc/articles/PMC9425080/ /pubmed/36051195 http://dx.doi.org/10.3389/fpsyg.2022.894576 Text en Copyright © 2022 Aznielle-Rodríguez, Ontivero-Ortega, Galán-García, Sahli and Valdés-Sosa. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Aznielle-Rodríguez, Tania
Ontivero-Ortega, Marlis
Galán-García, Lídice
Sahli, Hichem
Valdés-Sosa, Mitchell
Stable Sparse Classifiers predict cognitive impairment from gait patterns
title Stable Sparse Classifiers predict cognitive impairment from gait patterns
title_full Stable Sparse Classifiers predict cognitive impairment from gait patterns
title_fullStr Stable Sparse Classifiers predict cognitive impairment from gait patterns
title_full_unstemmed Stable Sparse Classifiers predict cognitive impairment from gait patterns
title_short Stable Sparse Classifiers predict cognitive impairment from gait patterns
title_sort stable sparse classifiers predict cognitive impairment from gait patterns
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425080/
https://www.ncbi.nlm.nih.gov/pubmed/36051195
http://dx.doi.org/10.3389/fpsyg.2022.894576
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