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

Abstract

Obstructive sleep apnea (OSA) is a common sleep disorder, and continuous positive airway pressure (CPAP) is the most effective treatment. Poor adherence is one of the major challenges in CPAP therapy. The recent boom of wearable optical sensors measuring oxygen saturation makes at-home multiple-night CPAP titrations possible, which may essentially improve the adherence of CPAP therapy by optimizing its pressure in a real-life setting economically. We tested whether the oxygen desaturations (ODs) measured in the arm muscle (arm_OD) by gold-standard frequency-domain multi-distance near-infrared spectroscopy (FDMD-NIRS) change quantitatively with titrated CPAP pressures in OSA patients together with polysomnography. We found that the arm_OD (2.08 ± 1.23%, mean ± standard deviation) was significantly smaller (p-value < 0.0001) than the fingertip OD (finger_OD) (4.46 ± 2.37%) measured by a polysomnography pulse oximeter. Linear mixed-effects models suggested that CPAP pressure was a significant predictor for finger_OD but not for arm_OD. Since FDMD-NIRS measures a mixture of arterial and venous OD, whereas a fingertip pulse oximeter measures arterial OD, our results of no association between arm_OD and finger_OD indicate that the arm_OD mainly represented venous desaturation. Arm_OD measured by optical sensors used for wearables may not be a suitable indicator of the CPAP titration effectiveness.

The impact of central and obstructive respiratory events on cerebral oxygenation in adults with sleep disordered breathing

“…The StO2 values smaller than 30% or larger than 90% were discarded to exclude the potential movement artifacts or unreliable recordings, considering that the normal NIRS StO2 baseline value is between 50% and 80% 42, 43 . The NIRS data were subjected to a low-pass (<0.08 Hz) zero-phase filter designed using Hanning window to remove the physiological noises including heart rate, respiratory noise and spontaneous slow hemodynamic oscillations 17, 22, 35, 40, 41, 44 . Then the filtered data were smoothed with moving average smooth method (robust locally weighted scatter plot smoothing 45 which assigns zero weight to data outside six mean absolute deviations) 17, 22, 35, 40, 41 .…”

“…The NIRS data were subjected to a low-pass (<0.08 Hz) zero-phase filter designed using Hanning window to remove the physiological noises including heart rate, respiratory noise and spontaneous slow hemodynamic oscillations 17, 22, 35, 40, 41, 44 . Then the filtered data were smoothed with moving average smooth method (robust locally weighted scatter plot smoothing 45 which assigns zero weight to data outside six mean absolute deviations) 17, 22, 35, 40, 41 . The coefficient of variation (CV, i.e., std./mean) of BV (CV-BV) during apnea/hypopnea and recovery phase was calculated.…”

“…Stepwise regression using backward elimination was done to automatically select the best explanatory variables by eliminating the non-significant covariates. Since there are different methods for calculating R 2 for LMM to measure its goodness of fit, we reported both the conditional R 2 calculated according to reference 47 and Ω 2 calculated according to reference 48 , which are two most frequently used R 2 , to assess the fitting quality of our final selected models 17, 41 .…”

“…The reliability of FDMD-NIRS measurement depended on the linearity of the raw optical signals on distances, i.e., the linear dependence R 2 of modulated light amplitude and phase shift over the measure distances should be highly close to 1 in each light wavelength. The raw optical data were discarded if the R 2 was smaller than 0.95 in either modulation amplitude or phase shift in any wavelength to exclude poorquality data due to the improper probe-skin contact and shunted light reaching the detector without travelling through the tissue 17,35,[39][40][41] . The StO2 values smaller than 30% or larger than 90% were discarded to exclude the potential movement artifacts or unreliable recordings, considering that the normal NIRS StO2 baseline value is between 50% and 80% 42,43 .…”

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

“…According to studies, 5%–15% of sleep apnea patients suffer from this type of sleep apnea (Lehman et al, 2007). In adults, sleep apnea syndrome is treated based on Continuous Positive Airway Pressure (CPAP), weight loss, and ultimately dental and surgical instruments (Baillieul et al, 2021; Z. Zhang et al, 2022). Initial treatment in children is Aden tonsillectomy.…”