The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study

Abstract

Background Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. About 90% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ≥30% drop in breathing and an at least 3% drop in peripheral capillary oxygen saturation (Spo2) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown. Objective We aimed to compare event-by-event arm OD (arm_OD) with fingertip OD (finger_OD) in sleep hypopneas during both naïve sleep and CPAP therapy. Methods Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH2O pressure per hour starting from 5 to 8 cmH2O depending on the individual). Arm_OD of the left biceps muscle and finger_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm_OD and finger_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed. Results In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57%) were collected during baseline sleep and 1517 (69.43%), during CPAP sleep. The mean difference between finger_OD and arm_OD was 2.86% (95% CI 2.67%-3.06%, t667=28.28; P<.001; 95% limits of agreement [LoA] –2.27%, 8.00%) during baseline sleep and 1.83% (95% CI 1.72%-1.94%, t1516=31.99; P<.001; 95% LoA –2.54%, 6.19%) during CPAP. Using the standard criterion of 3% saturation drop, arm_OD only recognized 16.32% (109/668) and 14.90% (226/1517) of hypopneas at baseline and during CPAP, respectively. Conclusions arm_OD is 2% to 3% lower than standard finger_OD in sleep hypopnea, probably because the measured arm_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ≥3% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness.

Quantitative Changes in Muscular and Capillary Oxygen Desaturation Measured by Optical Sensors during Continuous Positive Airway Pressure Titration for Obstructive Sleep Apnea

“…The Imagent system was calibrated on an optical phantom block to exclude the uncertainty of measurements due to machine errors before each recording. The raw measured NIRS data were subjected to a low-pass (<0.08 Hz) zero-phase filter designed using a Hanning window to remove the physiological noises including heart rate, respiratory noise and spontaneous slow hemodynamic oscillations [ 32 , 51 , 52 ]. The filtered data were then smoothed using the robust locally weighted scatter plot smoothing method [ 51 , 53 ].…”

“…After a standard PSG scoring, per-hour AHI under each CPAP pressure was calculated, i.e., the number of events was divided by the sleep duration under each CPAP pressure per hour in the titration protocol. Obstructive apneas ( n = 29) and hypopneas ( n = 31) were excluded from analysis if their SpO2 desaturations were greater than 15% to exclude outliers and potentially unreliable measurements caused by instrument errors [ 30 , 32 ]. In each patient, all the events under a specific CPAP pressure were also excluded if the corresponding sleep duration under that pressure was shorter than 20 min to exclude the unreliable calculation of the per-hour AHI.…”

“…Therefore, in this study, we quantified the dynamic changes in arm_OD in OSA events under titrated CPAP pressures using regression analysis. We used the same dataset that has been recently published in [32], considering its major advantages, which include: (1) the CPAP pressures were well-controlled stepwise and increased hourly during titration;…”

“…Measuring OSA by these low-cost wearable devices is unfortunately still not viable because none of them has been licensed or certified as a medical diagnostic device by the United States Food and Drug Administration or CE marking [ 31 ]. In our recent study [ 32 ], we compared event-by-event oxygen desaturation (OD) at the fingertip (finger_OD) measured by gold-standard in-lab PSG fingertip T-PPG with the OD at arm (arm_OD) measured by gold-standard frequency-domain multi-distance near-infrared spectroscopy (FDMD-NIRS) in OSA patients during naïve sleep and during CPAP titrations. Our Bland–Altman plots yielded poor agreement between finger_OD and arm_OD in sleep hypopneas, suggesting that the absolute value of arm_OD is not suitable to define sleep hypopneas according to the standard clinical criterion of ≥3% OD drop.…”

“…Therefore, in this study, we quantified the dynamic changes in arm_OD in OSA events under titrated CPAP pressures using regression analysis. We used the same dataset that has been recently published in [ 32 ], considering its major advantages, which include: (1) the CPAP pressures were well-controlled stepwise and increased hourly during titration; (2) the simultaneously measured finger_OD can serve as a control for CPAP titration effectiveness, i.e., an effective titration procedure should restore stepwise the fingertip SpO2 desaturation; (3) the comparisons between finger_OD (i.e., arterial desaturation) and arm_OD (i.e., the mixture of arterial and venous desaturation [ 38 , 39 ]) may provide new insights into the peripheral desaturation in response to OSA events during CPAP therapy. We hypothesized that the changes in arm_OD may be associated with changes in CPAP pressures, considering that our stepwise incremental CPAP titration gradually opened the upper airway to increase oxygen supply.…”