Quantitative detection of sleep apnea with wearable watch device

PLoS One. 2020 Nov 9;15(11):e0237279. doi: 10.1371/journal.pone.0237279. eCollection 2020.

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 <15 (6.4 ± 4.6 /h), and was able to discriminate them with 82% sensitivity, 89% specificity, and 85% accuracy. The classification performance was comparable to that obtained when the ACAT algorithm was applied to ECG R-R intervals during the PSG. The analysis of wearable watch PPG by the ACAT algorithm could be used for the quantitative screening of sleep apnea.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms*
  • Female
  • Heart Rate / physiology*
  • Humans
  • Male
  • Middle Aged
  • Monitoring, Ambulatory / instrumentation*
  • Polysomnography / instrumentation*
  • Quality of Life*
  • ROC Curve
  • Sleep Apnea Syndromes / diagnosis*
  • Wearable Electronic Devices / statistics & numerical data*

Grant support

This study is partly funded by WINFrontier Co., Ltd. This company participated in the study design and supported the data collection, but did not have any additional role in the data analysis, decision to publish, or preparation of the manuscript. The company also provided support in the form of salaries for two of the authors (M.K. and K.I.). The specific roles of these authors are articulated in the ‘author contributions’ section.