Characterizing Normal and Pathological Gait through Permutation Entropy

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

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Escuela Universitaria de Fisioterapia de la ONCE-UAM, 28034 Madrid, Spain

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Department of Physical Medicine and Rehabilitation, Hospital Universitario Infanta Sofía, San Sebastián de los Reyes, 28702 Madrid, Spain

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Servicio de Neuropediatría, Hospital Universitario La Paz, 28034 Madrid, Spain

Author to whom correspondence should be addressed.

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

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

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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. https://doi.org/10.3390/e20010077

AMA Style

Zanin M, Gómez-Andrés D, Pulido-Valdeolivas I, Martín-Gonzalo JA, López-López J, Pascual-Pascual SI, Rausell E. Characterizing Normal and Pathological Gait through Permutation Entropy. Entropy. 2018; 20(1):77. https://doi.org/10.3390/e20010077

Chicago/Turabian Style

Zanin, Massimiliano, David Gómez-Andrés, Irene Pulido-Valdeolivas, Juan A. Martín-Gonzalo, Javier López-López, Samuel I. Pascual-Pascual, and Estrella Rausell. 2018. "Characterizing Normal and Pathological Gait through Permutation Entropy" Entropy 20, no. 1: 77. https://doi.org/10.3390/e20010077

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Characterizing Normal and Pathological Gait through Permutation Entropy

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