Pulse Oximetry Monitor Feasible for Early Screening of Obstructive Sleep Apnea (OSA)

Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity

PURPOSE

This study aims to develop sleep apnea screening models with overnight SpO2 data, and to investigate the impact of the SpO2 data granularity on model performance.

METHODS

A total of 7,718 SpO2 recordings from the SHHS and MESA datasets were used. Probabilistic ensemble machine learning was employed to predict sleep apnea status at three AHI cutoff points: ≥ 5, ≥ 15, and ≥ 30 events/hour. To investigate the impact of data granularity, SpO2 data were aggregated at 30, 60, and 300 s.

RESULTS

Our models demonstrated good to excellent performance on internal test, with average area under the curve (AUC) values of 0.91, 0.93, and 0.96 for cutoffs ≥ 5, ≥ 15, and ≥ 30 at data granularity of 1 s, respectively. Both sensitivity (0.76, 0.84, 0.89) and specificity (0.87, 0.86, 0.90) ranged from good to excellent across three cutoffs. Positive predictive values (PPV) ranged from excellent to fair (0.97, 0.83, 0.66), and negative predictive values (NPV) ranged from low to excellent (0.43, 0.87, 0.98). Model performance on external test slightly dropped compared to internal test, but still achieved good to excellent AUC above 0.80 across all data granularity and all the three cutoffs. Data granularity of 300 s led to a reduction in performance metrics across all cutoffs.

CONCLUSION

Our models demonstrated superior performance across all three AHI cutoff thresholds compared to existing large sleep apnea screening models, even when considering varying SpO2 data granularity. However, lower data granularity was associated with decreased screening performance, indicating a need for further research in this area.

Portable evaluation of obstructive sleep apnea in adults: A systematic review

Obstructive sleep apnea (OSA) is a significant healthcare burden affecting approximately one billion people worldwide. The prevalence of OSA is rising with the ongoing obesity epidemic, a key risk factor for its development. While in-laboratory polysomnography (PSG) is the gold standard for diagnosing OSA, it has significant drawbacks that prevent widespread use. Portable devices with different levels of monitoring are available to allow remote assessment for OSA. To better inform clinical practice and research, this comprehensive systematic review evaluated diagnostic performances, study cost and patients' experience of different levels of portable sleep studies (type 2, 3, and 4), as well as wearable devices and non-contact systems, in adults. Despite varying study designs and devices used, portable diagnostic tests are found to be sufficient for initial screening of patients at risk of OSA. Future studies are needed to evaluate cost effectiveness with the incorporation of portable diagnostic tests into the diagnostic pathway for OSA, as well as their application in patients with chronic respiratory diseases and other comorbidities that may affect test performance.

A Flexible Thermocouple Film Sensor for Respiratory Monitoring

A novel flexible thermocouple film sensor on a polyimide substrate is proposed that is simple and flexible for monitoring the respiratory signal. Several thermocouples were connected in series and patterned on the polyimide substrate, and each one is formed by copper and a constant line connected to each other at two nodes. The respiratory signal was measured by the output voltage, which resulted from the temperature difference between the hot and cold junctions. The sensors were fabricated with surface-microfabrication technology with three sputtering steps. The measurement results showed that the peak voltage decreased by 90% in the case of apnea compared with normal breathing. The sensor has potential application for wearable detection of sleep apnea hypopnea syndrome (OSAHS).

Performance evaluation of a wrist-worn reflectance pulse oximeter during sleep

OBJECTIVES

To characterize and evaluate the estimation of oxygen saturation measured by a wrist-worn reflectance pulse oximeter during sleep.

METHODS

Ninety-seven adults with sleep disturbances were enrolled. Oxygen saturation was simultaneously measured using a reflectance pulse oximeter (Galaxy Watch 4 [GW4], Samsung, South Korea) and a transmittance pulse oximeter (polysomnography) as a reference. The performance of the device was evaluated using the root mean squared error (RMSE) and coverage rate. Additionally, GW4-derived oxygen desaturation index (ODI) was compared with the apnea-hypopnea index (AHI) derived from polysomnography.

RESULTS

The GW4 had an overall RMSE of 2.3% and negligible bias of -0.2%. A Bland-Altman density plot showed good agreement between the GW4 and the reference pulse oximeter. RMSEs were 1.65 ± 0.57%, 1.76 ± 0.65%, 1.93 ± 0.54%, and 2.93 ± 1.71% for normal (n = 18), mild (n = 21), moderate (n = 23), and severe obstructive sleep apnea (n = 35), respectively. The data rejection rate was 26.5%, which was caused by fluctuations in contact pressure and the discarding of data less than 70% of saturation. A GW4-ODI ≥5/h had the highest ability to predict AHI ≥15/h with sensitivity, specificity, accuracy, and area under the curve of 89.7%, 64.1%, 79.4%, and 0.908, respectively.

CONCLUSIONS

This study evaluated the estimation of oxygen saturation by the GW4 during sleep. This device complies with both Food and Drug Administration and International Organization for Standardization standards. Further improvements in the algorithms of wearable devices are required to obtain more accurate and reliable information about oxygen saturation measurements.

Diagnostic accuracy of oximetry for obstructive sleep apnea: a study on older adults in a home setting

OBJECTIVES

Owing to the fact that obstructive sleep apnea (OSA) is an underreported disease, the strategy used for the diagnosis of OSA has been extensively dissected to devise a simplified process that can be accessed by the public health services. Polysomnography (PSG) type I, the gold standard for the diagnosis of OSA, is expensive and difficult to access by low-income populations. In this study, we aimed to verify the accuracy of the oxyhemoglobin desaturation index (ODI) in comparison to the apnea-hypopnea index (AHI) using a portable monitor.

METHODS

We evaluated 94 type III PSG home test results of 65 elderly patients (69.21±6.94 years old), along with information, such as the body mass index (BMI) and sex, using data obtained from a clinical trial database.

RESULTS

A significant linear positive correlation (r=0.93, p<0.05) was observed between ODI and AHI, without any interference from sex, BMI, and positional component. The sensitivity of ODI compared to that of AHI increased with an increase in the severity of OSA, while the specificity of ODI in comparison to that of AHI was high for all degrees of severity. The accuracy of ODI was 80.7% for distinguishing between patients with mild and moderate apnea and 84.4% for distinguishing between patients with moderate and severe apnea.

CONCLUSION

The ODI values obtained in uncontrolled conditions exhibited high sensitivity for identifying severe apnea compared to the AHI values, and correctly identified the severity of OSA in more than 80% of the cases. Thus, oximetry is promising strategy for diagnosing OSA.

Design and Implementation of a Multifunction Wearable Device to Monitor Sleep Physiological Signals

We present a wearable device built on an Adafruit Circuit Playground Express (CPE) board and integrated with a photoplethysmographic (PPG) optical sensor for heart rate monitoring and multiple embedded sensors for medical applications-in particular, sleep physiological signal monitoring. Our device is portable and lightweight. Due to the microcontroller unit (MCU)-based architecture of the proposed device, it is scalable and flexible. Thus, with the addition of different plug-and-play sensors, it can be used in many applications in different fields. The innovation introduced in this study is that with additional sensors, we can determine whether there are intermediary variables that can be modified to improve our sleep monitoring algorithm. Additionally, although the proposed device has a relatively low cost, it achieves substantially improved performance compared to the commercially available Philips ActiWatch2 wearable device, which has been approved by the Food and Drug Administration (FDA). To assess the reliability of our device, we compared physiological sleep signals recorded simultaneously from volunteers using both our device and ActiWatch2. Motion and light detection data from our device were shown to be correlated to data simultaneously collected using the ActiWatch2, with correlation coefficients of 0.78 and 0.89, respectively. For 7 days of continuous data collection, there was only one instance of a false positive, in which our device detected a sleep interval, while the ActiWatch2 did not. The most important aspect of our research is the use of an open architecture. At the hardware level, general purpose input/output (GPIO), serial peripheral interface (SPI), integrated circuit (I²C), and universal asynchronous receiver-transmitter (UART) standards were used. At the software level, an object-oriented programming methodology was used to develop the system. Because the use of plug-and-play sensors is associated with the risk of adverse outcomes, such as system instability, this study heavily relied on object-oriented programming. Object-oriented programming improves system stability when hardware components are replaced or upgraded, allowing us to change the original system components at a low cost. Therefore, our device is easily scalable and has low commercialization costs. The proposed wearable device can facilitate the long-term tracking of physiological signals in sleep monitoring and related research. The open architecture of our device facilitates collaboration and allows other researchers to adapt our device for use in their own research, which is the main characteristic and contribution of this study.