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.
Highlights
Sleep apnea is a common health problem affecting 10–30% of adults [1, 2] and is associated with sleepiness, reduced sleep quality, reduced labor and learning capacity, frequent traffic accidents [3, 4], and increased cardiovascular disease risk [5]
Studies of pulse interval (PI) variability have reported important differences in the amplitude of short-term fluctuation components from those of R-R interval (RRI) variability [15,16,17,18,19,20], auto-correlated wave detection with adaptive threshold (ACAT) algorithm may be robust to such differences because the cycle length of cyclic variation of heart rate (CVHR) is long (25–130 s) and ACAT has ability to adapt the detection threshold according to the changes in CVHR amplitude
By applying the ACAT algorithm developed for the automated detection of sleep apnea from ECG, we investigated if sleep apnea can be detected from PPG signals obtained by a wearable watch device
Summary
Sleep apnea is a common health problem affecting 10–30% of adults [1, 2] and is associated with sleepiness, reduced sleep quality, reduced labor and learning capacity, frequent traffic accidents [3, 4], and increased cardiovascular disease risk [5]. Wearable watch devices with photoplethysmography (PPG) sensors have become popular Most of such devices still do not provide stable pulse wave signals during daily physical activities, they can generally deliver reliable signals during sleeping [8,9,10]. We examined if sleep apnea can be detected from PPG signals by applying an automated algorithm that has been developed for sleep apnea detection from R-R intervals of electrocardiography (ECG). The algorithm is called auto-correlated wave detection with adaptive threshold (ACAT) [11,12,13]. It detects the cyclic variations of heart rate (CVHR), a characteristic pattern of R-R interval variations accompanying sleep apnea episodes [14]. Studies of pulse interval (PI) variability have reported important differences in the amplitude of short-term fluctuation components from those of R-R interval (RRI) variability [15,16,17,18,19,20], ACAT algorithm may be robust to such differences because the cycle length of CVHR is long (25–130 s) and ACAT has ability to adapt the detection threshold according to the changes in CVHR amplitude
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