Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.

Journal: Sleep
PMID: 32436942

Abstract

STUDY OBJECTIVES: Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and having low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to develop an automatic method for identifying the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter.

Authors

  • Henri Korkalainen
  • Juhani Aakko
  • Brett Duce
  • Samu Kainulainen
  • Akseli Leino
  • Sami Nikkonen
    Department of Applied Physics, University of Eastern Finland, Kuopio, Finland. sami.nikkonen@kuh.fi.
  • Isaac O Afara
    Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
  • Sami Myllymaa
  • Juha Töyräs
    3 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
  • Timo Leppänen
    Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.

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