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Entropy 2018, 20(1), 77; https://doi.org/10.3390/e20010077

Characterizing Normal and Pathological Gait through Permutation Entropy

1
Center for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain
2
Department of Computer Science, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Lisboa, Portugal
3
MOVUAM-TRADESMA laboratory, Department of Anatomy, Histology and Neuroscience, Universidad Autónoma de Madrid, IdiPaz, 28029 Madrid, Spain
4
Paediatric Neurology Research Group, Hospital Universitari Vall d’Hebron, VHIR, 08035 Barcelona, Spain
5
Center of Neuroimmunology and Department of Neurology, Hospital Clínic of Barcelona, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Universitat de Barcelona, 08036 Barcelona, Spain
6
Escuela Universitaria de Fisioterapia de la ONCE-UAM, 28034 Madrid, Spain
7
Department of Physical Medicine and Rehabilitation, Hospital Universitario Infanta Sofía, San Sebastián de los Reyes, 28702 Madrid, Spain
8
Servicio de Neuropediatría, Hospital Universitario La Paz, 28034 Madrid, Spain
*
Author to whom correspondence should be addressed.
Received: 10 November 2017 / Revised: 15 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Abstract

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. View Full-Text
Keywords: permutation entropy; cerebral palsy; instrumental gait analysis permutation entropy; cerebral palsy; instrumental gait analysis
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Zanin, M.; Gómez-Andrés, D.; Pulido-Valdeolivas, I.; Martín-Gonzalo, J.A.; López-López, J.; Pascual-Pascual, S.I.; Rausell, E. Characterizing Normal and Pathological Gait through Permutation Entropy. Entropy 2018, 20, 77.

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