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Characterizing Normal and Pathological Gait through Permutation Entropy
Cerebral palsy is a physical impairment stemming from a brain lesion at perinatal time, most of the time resulting in gait abnormalities: the first cause of severe disability in childhood. Gait study, and instrumental gait analysis in particular, has been receiving increasing attention in the last f...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512275/ https://www.ncbi.nlm.nih.gov/pubmed/33265160 http://dx.doi.org/10.3390/e20010077 |
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author | Zanin, Massimiliano Gómez-Andrés, David Pulido-Valdeolivas, Irene Martín-Gonzalo, Juan Andrés López-López, Javier Pascual-Pascual, Samuel Ignacio Rausell, Estrella |
author_facet | Zanin, Massimiliano Gómez-Andrés, David Pulido-Valdeolivas, Irene Martín-Gonzalo, Juan Andrés López-López, Javier Pascual-Pascual, Samuel Ignacio Rausell, Estrella |
author_sort | Zanin, Massimiliano |
collection | PubMed |
description | Cerebral palsy is a physical impairment stemming from a brain lesion at perinatal time, most of the time resulting in gait abnormalities: the first cause of severe disability in childhood. Gait study, and instrumental gait analysis in particular, has been receiving increasing attention in the last few years, for being the complex result of the interactions between different brain motor areas and thus a proxy in the understanding of the underlying neural dynamics. Yet, and in spite of its importance, little is still known about how the brain adapts to cerebral palsy and to its impaired gait and, consequently, about the best strategies for mitigating the disability. In this contribution, we present the hitherto first analysis of joint kinematics data using permutation entropy, comparing cerebral palsy children with a set of matched control subjects. We find a significant increase in the permutation entropy for the former group, thus indicating a more complex and erratic neural control of joints and a non-trivial relationship between the permutation entropy and the gait speed. We further show how this information theory measure can be used to train a data mining model able to forecast the child’s condition. We finally discuss the relevance of these results in clinical applications and specifically in the design of personalized medicine interventions. |
format | Online Article Text |
id | pubmed-7512275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75122752020-11-09 Characterizing Normal and Pathological Gait through Permutation Entropy Zanin, Massimiliano Gómez-Andrés, David Pulido-Valdeolivas, Irene Martín-Gonzalo, Juan Andrés López-López, Javier Pascual-Pascual, Samuel Ignacio Rausell, Estrella Entropy (Basel) Article Cerebral palsy is a physical impairment stemming from a brain lesion at perinatal time, most of the time resulting in gait abnormalities: the first cause of severe disability in childhood. Gait study, and instrumental gait analysis in particular, has been receiving increasing attention in the last few years, for being the complex result of the interactions between different brain motor areas and thus a proxy in the understanding of the underlying neural dynamics. Yet, and in spite of its importance, little is still known about how the brain adapts to cerebral palsy and to its impaired gait and, consequently, about the best strategies for mitigating the disability. In this contribution, we present the hitherto first analysis of joint kinematics data using permutation entropy, comparing cerebral palsy children with a set of matched control subjects. We find a significant increase in the permutation entropy for the former group, thus indicating a more complex and erratic neural control of joints and a non-trivial relationship between the permutation entropy and the gait speed. We further show how this information theory measure can be used to train a data mining model able to forecast the child’s condition. We finally discuss the relevance of these results in clinical applications and specifically in the design of personalized medicine interventions. MDPI 2018-01-19 /pmc/articles/PMC7512275/ /pubmed/33265160 http://dx.doi.org/10.3390/e20010077 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zanin, Massimiliano Gómez-Andrés, David Pulido-Valdeolivas, Irene Martín-Gonzalo, Juan Andrés López-López, Javier Pascual-Pascual, Samuel Ignacio Rausell, Estrella Characterizing Normal and Pathological Gait through Permutation Entropy |
title | Characterizing Normal and Pathological Gait through Permutation Entropy |
title_full | Characterizing Normal and Pathological Gait through Permutation Entropy |
title_fullStr | Characterizing Normal and Pathological Gait through Permutation Entropy |
title_full_unstemmed | Characterizing Normal and Pathological Gait through Permutation Entropy |
title_short | Characterizing Normal and Pathological Gait through Permutation Entropy |
title_sort | characterizing normal and pathological gait through permutation entropy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512275/ https://www.ncbi.nlm.nih.gov/pubmed/33265160 http://dx.doi.org/10.3390/e20010077 |
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