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Quantitative detection of sleep apnea with wearable watch device

Author

Listed:
  • Junichiro Hayano
  • Hiroaki Yamamoto
  • Izumi Nonaka
  • Makoto Komazawa
  • Kenichi Itao
  • Norihiro Ueda
  • Haruhito Tanaka
  • Emi Yuda

Abstract

The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed by an automated algorithm called auto-correlated wave detection with adaptive threshold (ACAT) which was developed for electrocardiogram (ECG) to detect the cyclic variation of heart rate (CVHR), a characteristic heart rate pattern accompanying sleep apnea episodes. The median (IQR) apnea-hypopnea index (AHI) was 17.2 (4.4–28.4) and 22 (54%) subjects had AHI ≥15. The hourly frequency of CVHR (Fcv) detected by the ACAT algorithm closely correlated with AHI (r = 0.81), while none of the time-domain, frequency-domain, or non-linear indices of pulse interval variability showed significant correlation. The Fcv was greater in subjects with AHI ≥15 (19.6 ± 12.3 /h) than in those with AHI

Suggested Citation

  • Junichiro Hayano & Hiroaki Yamamoto & Izumi Nonaka & Makoto Komazawa & Kenichi Itao & Norihiro Ueda & Haruhito Tanaka & Emi Yuda, 2020. "Quantitative detection of sleep apnea with wearable watch device," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0237279
    DOI: 10.1371/journal.pone.0237279
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