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

Irene Pulido-Valdeolivas1,2, David Gómez-Andrés1,3, Juan Andrés Martín-Gonzalo1,4

  • 1Department of Anatomy, Histology and Neuroscience, TRADESMA-IdiPaz Universidad Autónoma de Madrid, Madrid, Spain.

Plos One
|March 9, 2018
PubMed

Insights

Hereditary Spastic Paraplegias (HSP) gait can be classified into six patterns using dynamic time warping, aiding in personalized therapy. Key indicators include pelvic tilt and hip flexion for gait pattern differentiation.

Area of Science:

  • Neurology
  • Biomechanical Engineering
  • Clinical Gait Analysis

Background:

  • Hereditary Spastic Paraplegias (HSP) encompass diverse neurological disorders causing varied gait abnormalities.
  • Current gait analysis methods offer limited insight into the entire gait cycle structure.
  • Phenotype classification is crucial for disease monitoring and tailored therapeutic interventions in HSP.

Purpose of the Study:

  • To classify gait phenotypes in children with HSP using advanced time series analysis.
  • To identify key kinematic parameters predictive of specific gait patterns and disease severity.
  • To provide clinicians with objective measures for daily practice and patient management.

Main Methods:

  • Acquisition of sagittal joint angular position data (pelvis, hip, knee, ankle, forefoot) using optokinetic instrumental gait analysis (IGA).
  • Application of hierarchical clustering analysis with multivariate dynamic time warping (DTW) to time series data.
  • Utilizing random forests to identify significant gait parameters for classification.

Main Results:

  • DTW identified six distinct gait patterns in HSP patients, correlating with age, sex, GMFCS stage, and comorbidities.
  • Mean pelvic tilt and hip flexion at initial contact emerged as critical differentiators between patterns.
  • Specific parameters like time of support, hip extension, and knee flexion at initial contact distinguished mild HSP from healthy gaits.

Conclusions:

  • A novel classification of HSP gait phenotypes is established through multivariate DTW.
  • Key kinematic parameters, particularly at initial contact, are identified for differentiating gait patterns and disease severity.
  • This classification framework supports objective patient assessment and personalized treatment strategies for HSP.

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