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Gait phenotypes in paediatric hereditary spastic paraplegia revealed by dynamic time warping analysis and random forests

Author
Pulido-Valdeolivas, Irene; Gómez-Andrés, David; Martín-Gonzalo, Juan Andrés; Rodríguez-Andonaegui, Irene; López-López, Javier; Pascual-Pascual, Samuel Ignacio; Rausell, Estrella
Entity
UAM. Departamento de Anatomía, Histología y Neurociencia; UAM. Departamento de Pediatría; Instituto de Investigación Sanitaria Hospital Universitario de La Paz (IdiPAZ)
Publisher
Public Library of Science
Date
2018-03-01
Citation
10.1371/journal.pone.0192345
PLoS ONE 13.3 (2018): e0192345
 
 
 
ISSN
1932-6203
DOI
10.1371/journal.pone.0192345
Funded by
DGA was in receipt of a grant from Sociedad Española de Neurología Pediátrica (SENEP). Publication fee was supported by EUF-ONCE-UAM and Asociación Española de Paraparesia Espástica Familiar (AEPEF).
Editor's Version
https://doi.org/10.1371/journal. pone.0192345
Subjects
Hereditary Spastic Paraplegias (HSP); Phenotypes; Gait analysis (IGA); Random forests; Medicina
URI
http://hdl.handle.net/10486/682501
Rights
© 2018 Pulido-Valdeolivas et al

Licencia Creative Commons
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.

Abstract

The Hereditary Spastic Paraplegias (HSP) are a group of heterogeneous disorders with a wide spectrum of underlying neural pathology, and hence HSP patients express a variety of gait abnormalities. Classification of these phenotypes May help in monitoring disease progression and personalizing therapies. This is currently managed by measuring values of some kinematic and spatio-temporal parameters at certain moments during the gait cycle, either in the doctor´s surgery room or after very precise measurements produced by instrumental gait analysis (IGA). These methods, however, do not provide information about the whole structure of the gait cycle. Classification of the similarities among time series of IGA measured values of sagittal joint positions throughout the whole gait cycle can be achieved by hierarchical clustering analysis based on multivariate dynamic time warping (DTW). Random forests can estimate which are the most important isolated parameters to predict the classification revealed by DTW, since clinicians need to refer to them in their daily practice. We acquired time series of pelvic, hip, knee, ankle and forefoot sagittal angular positions from 26 HSP and 33 healthy children with an optokinetic IGA system. DTW revealed six gait patterns with different degrees of impairment of walking speed, cadence and gait cycle distribution and related with patient’s age, sex, GMFCS stage, concurrence of polyneuropathy and abnormal visual evoked potentials or corpus callosum. The most important parameters to differentiate patterns were mean pelvic tilt and hip flexion at initial contact. Longer time of support, decreased values of hip extension and increased knee flexion at initial contact can differentiate the mildest, near to normal HSP gait phenotype and the normal healthy one. Increased values of knee flexion at initial contact and delayed peak of knee flexion are important factors to distinguish GMFCS stages I from II- III and concurrence of polyneuropathy
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Google™ Scholar:Pulido-Valdeolivas, Irene - Gómez-Andrés, David - Martín-Gonzalo, Juan Andrés - Rodríguez-Andonaegui, Irene - López-López, Javier - Pascual-Pascual, Samuel Ignacio - Rausell, Estrella

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  • Producción científica en acceso abierto de la UAM [15086]

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