Sway frequencies may predict postural instability in Parkinson's disease: a novel convolutional neural network approach.

Journal: Journal of neuroengineering and rehabilitation
Published Date: Feb 18, 2025

Abstract

BACKGROUND: Postural instability greatly reduces quality of life in people with Parkinson's disease (PD). Early and objective detection of postural impairments is crucial to facilitate interventions. Our aim was to use a convolutional neural network (CNN) to differentiate people with early to mid-stage PD from healthy age-matched individuals based on spectrogram images obtained from their body sway. We hypothesized the time-frequency content of body sway to be predictive of PD, even when impairments are not yet clinically apparent.

Authors

  • David Engel
    Applied Physics and Neurophysics, Philipps-Universität Marburg, Karl-von-Frisch-Straße 8a, Marburg, 35032, Germany. engelda@ohsu.edu.
  • R Stefan Greulich
    Chair of Business Information Systems, Esp. Intelligent Systems and Services, TUD Dresden University of Technology, Dresden, Germany.
  • Alberto Parola
    Department of Psychology, University of Turin, Turin, Italy.
  • Kaleb Vinehout
    Cold Spring Harbor Laboratory (CSHL), Cold Spring Harbor, NY, USA.
  • Justus Student
    Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany.
  • Josefine Waldthaler
    Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg, Marburg, Germany.
  • Lars Timmermann
    Department of Neurology, Philipps-University Marburg, Marburg, Germany.
  • Frank Bremmer
    Center for Mind, Brain and Behavior, Universities of Marburg, Giessen, and Darmstadt, Marburg, Germany.

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