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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Characterizing Normal and Pathological Gait through Permutation Entropy.

Massimiliano Zanin1,2, David Gómez-Andrés3,4, Irene Pulido-Valdeolivas3,5

  • 1Center for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

Researchers analyzed joint kinematics in children with cerebral palsy (CP) using permutation entropy. CP children showed more complex, erratic joint control, offering insights for personalized medicine and disability mitigation strategies.

Keywords:
cerebral palsyinstrumental gait analysispermutation entropy

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Information Theory

Background:

  • Cerebral palsy (CP) is a leading cause of childhood disability, characterized by gait abnormalities due to perinatal brain lesions.
  • Understanding the neural dynamics underlying CP gait is crucial for developing effective interventions.
  • Current knowledge on brain adaptation to CP and its impact on gait remains limited.

Purpose of the Study:

  • To investigate joint kinematics in children with CP compared to typically developing controls using permutation entropy.
  • To explore the relationship between permutation entropy, gait speed, and neural control in CP.
  • To assess the potential of permutation entropy for clinical applications and personalized medicine.

Main Methods:

  • Analysis of joint kinematics data from children with cerebral palsy and matched control subjects.
  • Application of permutation entropy, an information theory measure, to quantify complexity in joint control.
  • Development of a data mining model utilizing permutation entropy for condition forecasting.

Main Results:

  • Children with cerebral palsy exhibited a significant increase in permutation entropy compared to controls.
  • This finding indicates more complex and erratic neural control of joints in CP.
  • A non-trivial relationship was observed between permutation entropy and gait speed.

Conclusions:

  • Permutation entropy serves as a valuable measure for assessing neural control complexity in CP gait.
  • The findings support the use of permutation entropy in data mining models for CP condition forecasting.
  • Results highlight the potential for personalized medicine interventions in managing CP-related disabilities.