Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts.

Journal: Sleep medicine
Published Date: May 1, 2020

Abstract

OBJECTIVE: Currently, manual scoring is the gold standard of leg movement scoring (LMs) and periodic LMs (PLMS) in overnight polysomnography (PSG) studies, which is subject to inter-scorer variability. The objective of this study is to design and validate an end-to-end deep learning system for the automatic scoring of LMs and PLMS in sleep.

Authors

  • Lorenzo Carvelli
    Department of Health Technology, Technical University of Denmark, Kgs Lyngby, Denmark; Stanford University Center for Sleep Sciences and Medicine, Palo Alto, CA, USA.
  • Alexander N Olesen
    Center for Sleep Science and Medicine, Stanford University, Stanford, 94304, CA, USA.
  • Andreas Brink-Kjær
    Department of Health Technology, Technical University of Denmark, Kgs Lyngby, Denmark; Stanford University Center for Sleep Sciences and Medicine, Palo Alto, CA, USA; Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Glostrup, Denmark.
  • Eileen B Leary
    Center for Sleep Science and Medicine, Stanford University, Stanford, 94304, CA, USA.
  • Paul E Peppard
    Department of Preventive medicine, U Madison Wisconsin Madison, Wisconsin, USA.
  • Emmanuel Mignot
    Center for Sleep Sciences and Medicine, Stanford School of Medicine, Palo Alto, CA, USA.
  • Helge B D Sorensen
  • Poul Jennum
    Danish Center for Sleep Medicine, Rigshospitalet, Glostrup, 2600, Denmark.

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