Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.

Journal: Physiological measurement
Published Date: Feb 26, 2019

Abstract

OBJECTIVE: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P ) monitoring is the gold standard for measuring respiratory effort, but it is typically poorly tolerated because of its invasive nature. The objective was to investigate whether machine learning can be applied to routinely collected non-invasive, polysomnography (PSG) measures to accurately model peak negative P .

Authors

  • Umaer Hanif
    Stanford Center for Sleep Sciences and Medicine, Stanford University, 3165 Porter Drive, MC 5480, Palo Alto, CA 94304-5480, United States of America. Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, University of Copenhagen, Glostrup, Denmark. Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Logan D Schneider
  • Lotte Trap
  • Eileen B Leary
    Center for Sleep Science and Medicine, Stanford University, Stanford, 94304, CA, USA.
  • Hyatt Moore
  • Christian Guilleminault
  • Poul Jennum
    Danish Center for Sleep Medicine, Rigshospitalet, Glostrup, 2600, Denmark.
  • Helge B D Sorensen
  • Emmanuel J M Mignot

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