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.

Keywords: continuous positive airway pressure therapy; near-infrared spectroscopy; obstructive sleep apnea; oxygen saturation; photoplethysmography; smartwatch; wearable devices.

Figure 1

Beer–Lambert law and frequency-domain multidistance near-infrared spectroscopy (FDMD-NIRS) measurement. (A) The original Beer–lambert law describing the light propagation in a nonscattering absorbing media. The attenuation of light intensity in this absorbing media is proportional to the concentration C multiplied by the constant extinction coefficient ε for the particular absorber and the length d of the absorbing media. (B) The travelling of light in biological tissue (ie, highly scattering media). Light travels a longer pathway in the tissue than the light source-detector distance r due to scattering. (C) Basic principle of FDMD-NIRS measurement. The blue sine wave represents the high-frequency modulated light source. I_DC0 and I_AC0 are its light intensity and modulation amplitude, respectively. The two black sine waves are the output light detected after passing the measured tissues. They are detected by detectors 1 and 2 placed at different distances away from the light source. The light intensities and modulation amplitudes of the two black sine waves are smaller than those of the light source, and their phases are delayed because of the absorption and scattering in the tissues. Detector 1 is closer to the light source than detector 2. Therefore, the light intensity I_DC1 and modulation amplitude I_AC1 detected at detector 1 are larger than the light intensity I_DC2 and modulation amplitude I_AC2 detected at detector 2. The phase delay φ1 at detector 1 is smaller than the phase delay φ2 at detector 2 because the light reaching detector 2 has travelled a longer distance in the tissue. Similarly, the light intensity and modulation amplitude will be further decreased, and the phase delay will be further increased, when the light reaches the other detectors placed farther away than detector 2.

Figure 2

Typical oxygen desaturation (OD) at fingertip (finger_OD) and at arm (arm_OD) during hypopneas. Arrows indicate the degree of OD. SpO₂ is measured at the fingertip by polysomnography transmission photoplethysmography, and StO₂ is measured at the biceps muscle by frequency-domain multidistance near-infrared spectroscopy. SpO₂: peripheral capillary oxygen saturation; StO₂: peripheral tissue oxygen saturation.

Figure 3

Distributions of oxygen desaturation at fingertip (finger_OD) and at arm (arm_OD) at (A) baseline (n=668) and (B) under continuous positive airway pressure (CPAP: n=1517).

Figure 4

Bland-Altman plots of oxygen desaturation at fingertip (finger_OD) and at arm (arm_OD) at (A) baseline (n=668) and (B) under continuous positive airway pressure (CPAP: n=1517). The x-axes show the mean between the two measures, whereas the y-axes represent the differences (ie, finger_OD – arm_OD). The horizontal dotted lines indicate the mean difference and the 95% limits of agreement between the measures, ie, mean difference ± 1.96 × SD. The distribution of the mean difference is shown at the right margin of the plot, which is a normal distribution.

Figure 5

The correlations between oxygen desaturation at fingertip (finger_OD) and at arm (arm_OD) in the hypopnea events wherein finger_OD ≥ cut-off. X-axis shows the cut-off of finger_OD (from 2% to 8%). Y-axis depicts nonparametric Spearman’s correlation coefficient between finger_OD and arm_OD. The number of events used for correlation analysis and the P value are shown in the figure. The correlation analyses are performed for both baseline and CPAP sleep. For example, the green triangle at X=6, Y=0.3 means that during naïve baseline sleep there are 234 hypopneas associated with at least 6% finger_OD, and in these events, arm_OD weakly correlates to finger_OD with Spearman’s correlation coefficient equal to 0.3 and P<.001.