Application of deep learning to improve sleep scoring of wrist actigraphy.

Journal: Sleep medicine
Published Date: Oct 1, 2020

Abstract

BACKGROUND: Estimation of sleep parameters by wrist actigraphy is highly dependent on performance of the interpretative algorithm (IA) that converts movement data into sleep/wake scores.

Authors

  • Shahab Haghayegh
    Department of Biomedical Engineering Cockrell School of Engineering, The University of Texas at Austin, USA. Electronic address: shahab@utexas.edu.
  • Sepideh Khoshnevis
    Department of Biomedical Engineering Cockrell School of Engineering, The University of Texas at Austin, USA.
  • Michael H Smolensky
    Department of Biomedical Engineering Cockrell School of Engineering, The University of Texas at Austin, USA; Department of Internal Medicine, Division of Pulmonary and Sleep Medicine, McGovern School of Medicine the University of Texas Health Science Center at Houston, USA.
  • Kenneth R Diller
    Department of Biomedical Engineering Cockrell School of Engineering, The University of Texas at Austin, USA.

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