Key result
Photoplethysmography data from a wearable watch device analyzed by the ACAT algorithm correlated closely with the apnea-hypopnea index (r = 0.81) and detected moderate-to-severe sleep apnea with 82% sensitivity and 89% specificity.
Why the study?
Sleep apnea is a common, underdiagnosed condition linked to impaired quality of life and increased cardiovascular risk, prompting evaluation of whether wearable watch PPG pulse wave data can detect it.
Does a wearable watch device with PPG sensor analyzed by the ACAT algorithm accurately detect sleep apnea in patients undergoing diagnostic PSG?
Observational (n=41)
No
Does a wearable watch device with PPG sensor analyzed by the ACAT algorithm accurately detect sleep apnea in patients undergoing diagnostic PSG?
Effect estimate: r = 0.81
p-value: p=<0.0001
A wearable watch device using PPG and the ACAT algorithm can accurately detect sleep apnea, offering a potential quantitative screening tool.
Wearable photoplethysmography may aid sleep apnea detection; hypothesis-generating and requires larger validation before clinical use.
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.
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Hayano et al. (2020) conducted an observational in Sleep apnea (n=41). Photoplethysmography (PPG) via wearable watch device vs. Polysomnography (PSG) and ECG was evaluated on Correlation of PPG-derived cyclic variation of heart rate frequency (Fcv) with apnea-hypopnea index (AHI) (r = 0.81, p=<0.0001). Photoplethysmography data from a wearable watch device analyzed by the ACAT algorithm correlated closely with the apnea-hypopnea index (r = 0.81) and detected moderate-to-severe sleep apnea with 82% sensitivity and 89% specificity.
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