Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.

Journal: Journal of sport and health science
PMID: 36921692

Abstract

BACKGROUND: Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification.

Authors

  • Xianghao Zhan
    Department of Bioengineering, Stanford University, CA, 94305, USA.
  • Yiheng Li
    School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.
  • Yuzhe Liu
    School of Biological Science and Medical Engineering, BeiHang University, Beijing, 10019, China.
  • Nicholas J Cecchi
    Department of Bioengineering, Stanford University, CA, 94305, USA.
  • Samuel J Raymond
    Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
  • Zhou Zhou
  • Hossein Vahid Alizadeh
  • Jesse Ruan
    Tianjin University of Science and Technology, Tianjin, 300222, China.
  • Saeed Barbat
    The Ford Company, Dearborn, MI 48121, USA.
  • Stephen Tiernan
    Technological University Dublin, Dublin, D07 EWV4, Ireland.
  • Olivier Gevaert
    Department of Biomedical Data Science, Stanford University, CA, 94305, USA.
  • Michael M Zeineh
    Department of Radiology, Stanford University, CA, 94305, USA.
  • Gerald A Grant
    Departments of1Neurosurgery and.
  • David B Camarillo
    Department of Bioengineering, Stanford University, CA, 94305, USA.

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