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Updated: May 7, 2026
A Model to Simulate Clinically Relevant Hypoxia in Humans
Published on: December 22, 2016
Oxygen saturation in children with and without obstructive sleep apnea using the phone-oximeter
Insights
This study introduces a new method using a phone oximeter to detect obstructive sleep apnea (OSA) in children. The Phone Oximeter analyzes blood oxygen saturation (SpO2) to identify OSA, offering a more accessible diagnostic tool.
Area of Science:
- Pediatric Sleep Medicine
- Biomedical Engineering
- Medical Diagnostics
Background:
- Obstructive sleep apnea (OSA) in children presents significant health challenges, including daytime sleepiness and developmental issues.
- The current gold standard for OSA diagnosis, polysomnography (PSG), is resource-intensive and lab-bound.
- There is a need for accessible, at-home diagnostic methods for pediatric OSA.
Purpose of the Study:
- To develop and validate an algorithm using blood oxygen saturation (SpO2) data from a Phone Oximeter to identify children with OSA.
- To assess the feasibility of using a portable, in-home device for OSA screening in pediatric populations.
- To determine the effectiveness of SpO2 signal analysis in differentiating between children with and without OSA.
Main Methods:
- Utilized a Phone Oximeter to collect SpO2 data from 68 children (30 with OSA, 38 nonOSA) over multiple nights.
- Developed an algorithm analyzing SpO2 signals in time and frequency domains using a 90-second sliding window.
- Calculated spectral parameters (P, S, R) and temporal indices (desaturations, time below baseline) for feature selection.
Main Results:
- The algorithm achieved high diagnostic performance, with leave-one-out cross-validation yielding 86.8% accuracy, 80.0% sensitivity, and 92.1% specificity.
- A combination of 5 key parameters, including median R, mean P and S, and mean/SD of desaturations below 3% baseline, proved most effective.
- The study demonstrated the potential of SpO2 dynamics analysis for identifying pediatric OSA.
Conclusions:
- SpO2 monitoring with a Phone Oximeter offers a promising, non-invasive, and accessible approach for identifying obstructive sleep apnea in children.
- The developed algorithm effectively utilizes temporal and spectral SpO2 signal characteristics for OSA detection.
- This method could reduce the reliance on resource-intensive PSG, improving OSA diagnosis accessibility in pediatric care.
Abstract:
Obstructive sleep apnea (OSA) in children can lead to daytime sleepiness, growth failure and developmental delay. Polysomnography (PSG), the gold standard to diagnose OSA is highly resource intensive and is confined to the sleep laboratory. In this study we propose to identify children with OSA using blood oxygen saturation (SpO2) obtained from the Phone Oximeter. This portable, in-home device is able to monitor patients over multiple nights, causes less sleep disturbance and facilitates a more natural sleep pattern. The proposed algorithm analyzes the SpO2 signal in the time and frequency domain using a 90-s sliding window. Three spectral parameters are calculated from the power spectral density (PSD) to evaluate the modulation in the SpO2 due to the oxyhemoblobin desaturations. The power P, slope S in the discriminant band (DB), and ratio R between P and total power are calculated for each window. Tendency and variability indices, number of SpO2 desaturations and time spent under 2% or 3% of baseline saturation level are computed for each time window. The statistical distribution of the temporal evolution of all parameters is analyzed to identify 68 children, 30 with OSA and 38 without OSA (nonOSA). This characterization was evaluated by a feature selection based on a linear discriminant. The combination of temporal and spectral parameters provided the best leave one out crossvalidation results with an accuracy of 86.8%, a sensitivity of 80.0%, and a specificity of 92.1% using only 5 parameters. The median of R, mean of P and S and mean and standard deviation of the number of desaturations below 3% of baseline saturation level, were the most representative parameters. Hence, a better knowledge of SpO2 dynamics could help identifying children with OSA with the Phone Oximeter.
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