Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Journal: Sleep
PMID: 39301948

Abstract

STUDY OBJECTIVES: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

Authors

  • Teitur Óli Kristjánsson
    Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Katie L Stone
    California Pacific Medical Center, Research Institute, San Francisco.
  • Helge B D Sorensen
  • 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.
  • Emmanuel Mignot
    Center for Sleep Sciences and Medicine, Stanford School of Medicine, Palo Alto, CA, USA.
  • Poul Jennum
    Danish Center for Sleep Medicine, Rigshospitalet, Glostrup, 2600, Denmark.

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