A Comparison of Signal Combinations for Deep Learning-Based Simultaneous Sleep Staging and Respiratory Event Detection.

Journal: IEEE transactions on bio-medical engineering
Published Date: Apr 20, 2023

Abstract

OBJECTIVE: Obstructive sleep apnea (OSA) is diagnosed using the apnea-hypopnea index (AHI), which is the average number of respiratory events per hour of sleep. Recently, machine learning algorithms for automatic AHI assessment have been developed, but many of them do not consider the individual sleep stages or events. In this study, we aimed to develop a deep learning model to simultaneously score both sleep stages and respiratory events. The hypothesis was that the scoring and subsequent AHI calculation could be performed utilizing pulse oximetry data only.

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
  • Erna S Arnardottir
  • Sami Nikkonen
    Department of Applied Physics, University of Eastern Finland, Kuopio, Finland. sami.nikkonen@kuh.fi.
  • Sami Myllymaa
  • Juha Töyräs
    3 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
  • Henri Korkalainen

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