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Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry

Author

Listed:
  • Jeremy Levy

    (Technion-IIT
    Israel Institute of Technology)

  • Daniel Álvarez

    (Río Hortega University Hospital Valladolid
    University of Valladolid
    Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN))

  • Félix Campo

    (Río Hortega University Hospital Valladolid
    University of Valladolid
    Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN))

  • Joachim A. Behar

    (Israel Institute of Technology)

Abstract

Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark.

Suggested Citation

  • Jeremy Levy & Daniel Álvarez & Félix Campo & Joachim A. Behar, 2023. "Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40604-3
    DOI: 10.1038/s41467-023-40604-3
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