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

  title={Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.},
  author={Umaer Hanif and Logan D Schneider and Lotte Trap and Eileen B. Leary and Hyatt E. Moore and C Guilleminault and Poul J{\o}rgen Jennum and Helge Bjarup Dissing S{\o}rensen and Emmanuel Mignot},
  journal={Physiological measurement},
  volume={40 2},
OBJECTIVE Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P es) 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 es. APPROACH One… Expand
1 Citations


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