Assessment of obstructive sleep apnea-related sleep fragmentation utilizing deep learning-based sleep staging from photoplethysmography.

Journal: Sleep
PMID: 34089616

Abstract

STUDY OBJECTIVES: To assess the relationship between obstructive sleep apnea (OSA) severity and sleep fragmentation, accurate differentiation between sleep and wakefulness is needed. Sleep staging is usually performed manually using electroencephalography (EEG). This is time-consuming due to complexity of EEG setup and the amount of work in manual scoring. In this study, we aimed to develop an automated deep learning-based solution to assess OSA-related sleep fragmentation based on photoplethysmography (PPG) signal.

Authors

  • Riku Huttunen
    Faculty of Medicine and Health Technology, Tampere University, FI-33520 Tampere, Finland.
  • Timo Leppänen
    Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
  • Brett Duce
  • Arie Oksenberg
    Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel.
  • Sami Myllymaa
  • Juha Töyräs
    3 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
  • Henri Korkalainen

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