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<p>Despite evidence of early neurodegeneration, postural instability is commonly associated with later stages of Parkinson’s disease (PD), mainly due to a lack of sensitive measures. Here, we aim to provide a sensitive, easily obtainable objective measure of postural instability for earlier clinical detection. We assessed postural sway in 40 newly diagnosed, untreated individuals with PD and 79 age-matched healthy controls while they stood quietly for 30 seconds with their eyes open and feet together. Body sway was recorded with a single accelerometer placed at the lumbar spine. We trained a convolutional neural network (CNN) to distinguish between the groups based on the frequency information of their sway signals. Our models reached an average accuracy, sensitivity, and specificity of 98.9%, 97.7%, and 98.9%, respectively. This suggests that characteristic frequency features of postural sway reflect subtle postural impairments in early PD, with great potential to translate into clinical applications.</p>

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Frequency-based deep learning to identify subtle postural instability in early, untreated Parkinson’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月27日

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  • David Engel,
  • Pablo Burgos,
  • Patricia Carlson-Kuhta,
  • Lauren Talman,
  • Joseph F. Quinn,
  • Kaleb Vinehout,
  • R. Stefan Greulich,
  • Fay B. Horak,
  • Martina Mancini

摘要

Despite evidence of early neurodegeneration, postural instability is commonly associated with later stages of Parkinson’s disease (PD), mainly due to a lack of sensitive measures. Here, we aim to provide a sensitive, easily obtainable objective measure of postural instability for earlier clinical detection. We assessed postural sway in 40 newly diagnosed, untreated individuals with PD and 79 age-matched healthy controls while they stood quietly for 30 seconds with their eyes open and feet together. Body sway was recorded with a single accelerometer placed at the lumbar spine. We trained a convolutional neural network (CNN) to distinguish between the groups based on the frequency information of their sway signals. Our models reached an average accuracy, sensitivity, and specificity of 98.9%, 97.7%, and 98.9%, respectively. This suggests that characteristic frequency features of postural sway reflect subtle postural impairments in early PD, with great potential to translate into clinical applications.