Detecting arousals and sleep from respiratory inductance plethysmography.

Journal: Sleep & breathing = Schlaf & Atmung
PMID: 40214714

Abstract

PURPOSE: Accurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polysomnography (PSG) is the gold standard for such assessments but is costly and requires overnight monitoring in a lab. Home sleep testing (HST) offers a more accessible alternative, relying primarily on breathing measurements but lacks electroencephalography, limiting its ability to evaluate sleep and arousals directly. This study evaluates a deep learning algorithm which determines sleep states and arousals from breathing signals.

Authors

  • Eysteinn Finnsson
    Nox Research, Nox Medical, Katrínartún 2, 105, Reykjavík, Iceland. eysteinnf@noxmedical.com.
  • Ernir Erlingsson
    Nox Research, Nox Medical, Katrínartún 2, 105, Reykjavík, Iceland.
  • Hlynur D Hlynsson
    Nox Research, Nox Medical, Katrínartún 2, 105, Reykjavík, Iceland.
  • Vaka Valsdóttir
    Nox Research, Nox Medical, Katrínartún 2, 105, Reykjavík, Iceland.
  • Thora B Sigmarsdottir
    Nox Research, Nox Medical, Katrínartún 2, 105, Reykjavík, Iceland.
  • Eydís Arnardóttir
    Nox Research, Nox Medical, Katrínartún 2, 105, Reykjavík, Iceland.
  • Scott A Sands
    Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Sigurður Æ Jónsson
    Nox Research, Nox Medical, Katrínartún 2, 105, Reykjavík, Iceland.
  • Anna S Islind
    Department of Computer Science, Reykjavik University, Reykjavik, Iceland.
  • Jón S Ágústsson
    Nox Research, Nox Medical, Katrínartún 2, 105, Reykjavík, Iceland.

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