Oxygen saturation in children with and without obstructive sleep apnea using the phone-oximeter

Modelling metabolic performance in paediatric obstructive sleep disordered breathing: A case-control study

Paediatric obstructive sleep disordered breathing (OSDB) has a considerable impact on cardiovascular physiology, but the consequences on children's basal metabolism and response to exercise are far from being known. The objective was to propose model estimations for paediatric OSDB metabolism at rest and during exercise. A retrospective case-control analysis of data from children submitted to otorhinolaryngology surgery was performed. The heart rate (HR) was measured, while oxygen consumption (VO2) and energy expenditure (EE) at rest and during exercise were obtained using predictive equations. The results for the patients with OSDB were compared with controls. A total of 1256 children were included. A total of 449 (35.7%) had OSDB. The patients with OSDB showed a significantly higher resting heart rate (94.55 ± 15.061 bpm in OSDB vs. 92.41 ± 15.332 bpm in no-OSDB, p = 0.041). The children with OSDB showed a higher VO2 at rest (13.49 ± 6.02 mL min-1kg-1 in OSDB vs. 11.55 ± 6.83 mL min-1kg-1 in no-OSDB, p = 0.004) and a higher EE at rest (67.5 ± 30.10 cal min-1kg-1 in OSDB vs. 57.8 + 34.15 cal min-1kg-1 in no-OSDB, p = 0.004). At maximal exercise, patients with OSDB showed a lower VO2max (33.25 ± 5.82 mL min-1kg-1 in OSDB vs. 34.28 ± 6.71 in no-OSDB, p = 0.008) and a lower EE (166.3 ± 29.11 cal min-1kg-1 in OSDB vs. 171.4 ± 33.53 cal min-1kg-1 in no-OSDB, p = 0.008). The VO2/EE increment with exercise (Δ VO2 and Δ EE) was lower in OSDB for all exercise intensities (p = 0.009). This model unveils the effect of paediatric OSDB on resting and exercise metabolism. Our findings support the higher basal metabolic rates, poorer fitness performance, and cardiovascular impairment found in children with OSDB.

The Role of Telemedicine in Children with Obstructive Sleep Apnea Syndrome (OSAS): A Review of the Literature

The advent of telemedicine marked a significant turning point in the healthcare landscape, introducing a revolutionary approach to the delivery of medical care. Digital technologies easily connect health professionals and patients, overcoming geographical and temporal barriers. Telemedicine has been used for sleep disorders including obstructive sleep apnea syndrome (OSAS) since the mid-1990s. In adult patients with OSAS, telemedicine is helpful both for consultation and diagnosis, the latter obtained through remote recordings of oxygen saturation and further parameters registered with telemonitored respiratory polygraphy or polysomnography. Remote monitoring can be used to follow up the patient and verify adherence to daily treatments including continuous positive airway pressure (CPAP). In children, studies on the role of telemedicine in OSAS are scarce. This narrative review aims to describe the application of telemedicine in children with obstructive sleep apnea syndrome (OSAS), assessing its advantages and disadvantages. In patients with OSA, telemedicine is applicable at every stage of patient management, from diagnosis to treatment monitoring also in pediatric and adolescent ages. While telemedicine offers convenience and accessibility in healthcare delivery, its application in managing OSAS could be associated with some disadvantages, including limitations in physical examination, access to diagnostic tools, and education and counseling; technology barriers; and privacy concerns. The adoption of a hybrid approach, integrating both in-office and virtual appointments, could effectively meet the needs of children with OSAS. However, more studies are needed to fully assess the effectiveness and safety of telemedicine in the pediatric population.

Can portable sleep monitors replace polysomnography for diagnosis of pediatric OSA: a systematic review and meta-analysis

BACKGROUND

Obstructive sleep apnoea (OSA) is an increasing health problem in children. The "gold standard" for OSA diagnosis at the moment is overnight polysomnography (PSG). Some researchers think portable monitors (PMs) are promising methods for diagnosing OSA, which make children more comfortable and lower costs. Compared with PSG, our comprehensively evaluated the diagnostic accuracy of PMs for diagnosing OSA in pediatrics.

RESEARCH QUESTION

This study aims to determine whether PMs can replace PSG in pediatric OSA diagnosis.

STUDY DESIGN AND METHODS

The PubMed, Embase, Medline databases Scopus, Web of Science, and Cochrane Library databases were searched systematically for studies published up to December 2022, evaluating the ability of PMs to diagnose OSA in children. For estimating the pooled sensitivity and specificity of the PMs in the included studies, we used a random-effects bivariate model. Studies included in this meta-analysis were evaluated systematically according to QUADAS-2 guidelines for assessing diagnostic accuracy studies. Two independent investigators conducted each stage of the review independently.

RESULTS

A total of 396 abstracts and 31 full-text articles were screened, and 41 full-text articles were chosen for final review. There were 707 pediatric patients enrolled in these twelve studies, and 9 PMs were evaluated. There was a wide range of diagnostic sensitivity and specificity among PM systems as compared to AHI measured by PSG. The pooled sensitivity and specificity in diagnosing pediatric OSA were, respectively, 0.91 [0.86, 0.94] and 0.76 [0.58, 0.88] for PMs. According to the summary receiver operating characteristic (SROC) curve, the AUC of PMs in diagnosing OSA in pediatric population was 0.93 [0.90, 0.95].

INTERPRETATION

PMs were more sensitive but slightly less specific for pediatric OSA. The combination of PMs and questionnaires appeared to be a reliable tool for the diagnosis of pediatric OSA. This test may be used for screening subjects or populations at high risk of OSA when there is a high demand for PSG, but the quantity is limited. No clinical trial was involved in the current study.

Pulse oximetrySpO2signal for automated identification of sleep apnea: a review and future trends

Sleep apnea (SA) is characterized by intermittent episodes of apnea or hypopnea paused or reduced breathing, respectively each lasting at least ten seconds that occur during sleep. SA has an estimated global prevalence of 200 million and is associated with medical comorbidity, and sufferers are also more likely to sustain traffic- and work-related injury due to daytime somnolence. SA is amenable to treatment if detected early. Polysomnography (PSG) involving multi-channel signal acquisition is the reference standard for diagnosing SA but is onerous and costly. For home-based detection of SA, single-channelSpO₂signal acquisition using portable pulse oximeters is feasible. Machine (ML) and deep learning (DL) models have been developed for automated classification of SA versus no SA usingSpO₂signals alone. In this work, we review studies published between 2012 and 2022 on the use of ML and DL forSpO₂signal-based diagnosis of SA. A literature search based on PRISMA recommendations yielded 297 publications, of which 31 were selected after considering the inclusion and exclusion criteria. There were 20 ML and 11 DL models; their methods, differences, results, merits, and limitations were discussed. Many studies reported encouraging performance, which indicates the utility ofSpO₂signals in wearable devices for home-based SA detection.

Night to night variability of pulse oximetry features in children at home and at the hospital

Objective. Investigation of the night-to-night (NtN) variability of pulse oximetry features in children with suspicion of Sleep Apnea.Approach. Following ethics approval and informed consent, 75 children referred to British Columbia Children's Hospital for overnight PSG were recorded on three consecutive nights, including one at the hospital simultaneously with polysomnography and 2 nights at home. During all three nights, a smartphone-based pulse oximeter sensor was used to record overnight pulse oximetry (SpO2 and photoplethysmogram). Features characterizing SpO2 dynamics and heart rate were derived. The NtN variability of these features over the three different nights was investigated using linear mixed models.Main results. Overall most pulse oximetry features (e.g. the oxygen desaturation index) showed no NtN variability. One of the exceptions is for the signal quality, which was significantly lower during at home measurements compared to measurements in the hospital.Significance. At home pulse oximetry screening shows an increasing predictive value to investigate obstructive sleep apnea (OSA) severity. Hospital recordings affect subjects normal sleep and OSA severity and recordings may vary between nights at home. Before establishing the role of home monitoring as a diagnostic test for OSA, we must first determine their NtN variability. Most pulse oximetry features showed no significant NtN variability and could therefore be used in future at-home testing to create a reliable and consistent OSA screening tool. A single night recording at home should be able to characterize pulse oximetry features in children.

Detecting obstructive sleep apnea in children by self-affine visualization of oximetry

Obstructive sleep apnea (OSA), characterized by cessations of breathing during sleep due to upper airway collapse, can affect the healthy growth and development of children. The gold standard for OSA diagnosis, polysomnography(PSG), is expensive and resource intensive, resulting in long waiting lists to perform a PSG. Previously, we investigated the time-frequency analysis of blood oxygen saturation (SpO₂) to screen for OSA. We used overnight pulse oximetry from 146 children, collected using a smartphone-based pulse oximeter (Phone Oximeter), simultaneously with standard PSG. Sleep technicians manually scored PSG and provided the average of apnea/hypoapnea events per hour (AHI). In this study, we proposed an alternative method for analyzing SpO₂, in which a set of contracting transformations form a self-affine set with a 2D attractor, previously developed for qualitative visualization of the photoplethysmogram and electroencephalogram. We applied this technique to the overnight SpO₂ signal from individual patients and extracted features based on the distribution of points (radius and angle) in the visualization. The cloud of points in children without OSA (NonOSA) was more confined than in children with OSA, which was reflected by more empty pixels (radius and angles). The maximum value, skewness and standard deviation of the distribution of points located at different radius and angles were significantly (Bonferroni corrected) higher in NonOSA compared to OSA children. To detect OSA defined at different levels (AHI≥5, AHI≥10 and AHI≥15), three multivariate logistic regression models were implemented using a stepwise feature selection and internally validated through bootstrapping. The models (AHI≥5, AHI≥10, AHI≥15), consisting of 3, 4 and 1 features respectively, provided a bootstrap-corrected AUC of 73%, 81%, 73%. Thus, applying this visualization to nocturnal SpO₂ could yield both visual and quantitative information that might be useful for screening children for OSA.

Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children

Obstructive sleep apnea (OSA) disrupts normal ventilation during sleep and can lead to serious health problems in children if left untreated. Polysomnography, the gold standard for OSA diagnosis, is resource intensive and requires a specialized laboratory. Thus, we proposed to use the Phone Oximeter™, a portable device integrating pulse oximetry with a smartphone, to detect OSA events. As a proportion of OSA events occur without oxygen desaturation (defined as SpO2 decreases ≥ 3%), we suggest combining SpO2 and pulse rate variability (PRV) analysis to identify all OSA events and provide a more detailed sleep analysis. We recruited 160 children and recorded pulse oximetry consisting of SpO2 and plethysmography (PPG) using the Phone Oximeter™, alongside standard polysomnography. A sleep technician visually scored all OSA events with and without oxygen desaturation from polysomnography. We divided pulse oximetry signals into 1-min signal segments and extracted several features from SpO2 and PPG analysis in the time and frequency domain. Segments with OSA, especially the ones with oxygen desaturation, presented greater SpO2 variability and modulation reflected in the spectral domain than segments without OSA. Segments with OSA also showed higher heart rate and sympathetic activity through the PRV analysis relative to segments without OSA. PRV analysis was more sensitive than SpO2 analysis for identification of OSA events without oxygen desaturation. Combining SpO2 and PRV analysis enhanced OSA event detection through a multiple logistic regression model. The area under the ROC curve increased from 81% to 87%. Thus, the Phone Oximeter™ might be useful to monitor sleep and identify OSA events with and without oxygen desaturation at home.

Polysomnographic correlates of inflammatory complement components in young healthy males

A growing body of evidence has delineated the predominant role of humoral mediators of inflammation in linking sleep with immunity. Nonetheless, characterization of the relationship between complement components with inflammatory functions and objective sleep measures has not been performed. In this study we investigated the relationships between objective measures of sleep and complement components with inflammatory functions. Thirty-six healthy male university students (age, 23.94±4.23 years; BMI, 23.44±2.67 kg/m(2)) completed the study. An RMS Quest 32 polysomnograph (PSG) was used for sleep recording. Non-fasting blood was collected before subjects went to bed on the second night in the sleep laboratory to estimate complement component 3 (C-3), complement component 4 (C-4), complement factor-H (Factor-H), C1-inhibitor (C1INH), complement factor I (CFI) and other inflammatory mediators, such as IL-6 and sICAM-1. Multiple linear regression analysis was used to assess the association between PSG sleep measures and inflammatory mediators. Higher values of C-3 and lower values of sICAM-1, C1INH, and CFI (adjusted model, R2=0.211, p<0.041) predicted longer sleep duration. Lower C-3 (adjusted model, R2=0.078, p<0.055) predicted higher N1 (%). Higher levels of C1INH and CFI and lower values of C-4 (model adjusted R2=0.269, p<0.008) predicted higher N3 (%). Higher C-3, higher C-4, lower IL-6, lower C1INH and lower CFI (model adjusted R2=0.296, p<0.007) predicted higher REM (%). Poor sleep measures were associated with increased levels of pro-inflammatory complement components and decreased anti-inflammatory complement components.

Pediatric Home Sleep Apnea Testing: Slowly Getting There!

Pediatric OSA can result in significant neurocognitive, behavioral, cardiovascular, and metabolic morbidities. Prompt diagnosis and treatment are, therefore, of paramount importance. The current gold standard for diagnosis of OSA in children is in-laboratory polysomnography (PSG). Home sleep apnea testing has been considered as an alternative as it is potentially more cost effective, convenient, and accessible. This review concentrates mainly on the use of type 2 and 3 portable monitoring devices. The current evidence on the feasibility and diagnostic accuracy of home testing in the diagnosis of pediatric OSA was examined. Overall, the evidence in children is limited. Feasibility studies that have been performed have on the whole shown good results, with several reporting > 90% of their home recordings as meeting predetermined quality criteria regarding signal artifact and minimum recording time. The limited data comparing type 2 studies with in-laboratory PSG have shown no significant differences in respiratory parameters. The results pertaining to diagnostic accuracy of type 3 home sleep apnea testing devices are conflicting. Although more research is needed, home testing with at least a type 3 portable monitor offers a viable alternative in the diagnosis of otherwise healthy children with moderate to severe OSA, particularly in settings where access to polysomnography is scarce or unavailable. Of note, since most studies have been performed in habitually snoring healthy children, home sleep apnea testing may not be applicable to children with other comorbid conditions. In particular, CO2 monitoring is important in children in whom there is concern regarding nocturnal hypoventilation, such as children with neuromuscular disease, underlying lung disease, or obesity hypoventilation, and most home testing devices do not include a transcutaneous or end-tidal CO2 channel.

Oxygen saturation resolution influences regularity measurements

The measurement of regularity in the oxygen saturation (SpO(2)) signal has been suggested for use in identifying subjects with sleep disordered breathing (SDB). Previous work has shown that children with SDB have lower SpO(2) regularity than subjects without SDB (NonSDB). Regularity was measured using non-linear methods like approximate entropy (ApEn), sample entropy (SamEn) and Lempel-Ziv (LZ) complexity. Different manufacturer's pulse oximeters provide SpO(2) at various resolutions and the effect of this resolution difference on SpO(2) regularity, has not been studied. To investigate this effect, we used the SpO(2) signal of children with and without SDB, recorded from the Phone Oximeter (0.1% resolution) and the same SpO(2) signal rounded to the nearest integer (artificial 1% resolution). To further validate the effect of rounding, we also used the SpO(2) signal (1% resolution) recorded simultaneously from polysomnography (PSG), as a control signal. We estimated SpO(2) regularity by computing the ApEn, SamEn and LZ complexity, using a 5-min sliding window and showed that different resolutions provided significantly different results. The regularity calculated using 0.1% SpO(2) resolution provided no significant differences between SDB and NonSDB. However, the artificial 1% resolution SpO(2) provided significant differences between SDB and NonSDB, showing a more random SpO(2) pattern (lower SpO(2) regularity) in SDB children, as suggested in the past. Similar results were obtained with the SpO(2) recorded from PSG (1% resolution), which further validated that this SpO(2) regularity change was due to the rounding effect. Therefore, the SpO(2) resolution has a great influence in regularity measurements like ApEn, SamEn and LZ complexity that should be considered when studying the SpO(2) pattern in children with SDB.

Exploring the spectral information of airflow recordings to help in pediatric Obstructive Sleep Apnea-Hypopnea Syndrome diagnosis

This work aims at studying the usefulness of the spectral information contained in airflow (AF) recordings in the context of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) in children. To achieve this goal, we defined two spectral bands of interest related to the occurrence of apneas and hypopneas. We characterized these bands by extracting six common spectral features from each one. Two out of the 12 features reached higher diagnostic ability than the 3% oxygen desaturation index (ODI3), a clinical parameter commonly used as screener for OSAHS. Additionally, the stepwise logistic regression (SLR) feature-selection algorithm showed that the information contained in the two bands was complementary, both between them and with ODI3. Finally, the logistic regression method involving spectral features from the two bands, as well as ODI3, achieved high diagnostic performance after a bootstrap validation procedure (84.6±9.6 sensitivity, 87.2±9.1 specificity, 85.8±5.2 accuracy, and 0.969±0.03 area under ROC curve). These results suggest that the spectral information from AF is helpful to detect OSAHS in children.

Statistical and nonlinear analysis of oximetry from respiratory polygraphy to assist in the diagnosis of Sleep Apnea in children

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep related breathing disorder that has important consequences in the health and development of infants and young children. To enhance the early detection of OSAHS, we propose a methodology based on automated analysis of nocturnal blood oxygen saturation (SpO(2)) from respiratory polygraphy (RP) at home. A database composed of 50 SpO(2) recordings was analyzed. Three signal processing stages were carried out: (i) feature extraction, where statistical features and nonlinear measures were computed and combined with conventional oximetric indexes, (ii) feature selection using genetic algorithms (GAs), and (iii) feature classification through logistic regression (LR). Leave-one-out cross-validation (loo-cv) was applied to assess diagnostic performance. The proposed method reached 80.8% sensitivity, 79.2% specificity, 80.0% accuracy and 0.93 area under the ROC curve (AROC), which improved the performance of single conventional indexes. Our results suggest that automated analysis of SpO(2) recordings from at-home RP provides essential and complementary information to assist in OSAHS diagnosis in children.

Development of a screening tool for sleep disordered breathing in children using the phone Oximeter™

Background

Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory.

Aim

To combine the blood oxygen saturation (SpO₂) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone.

Methods

Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO₂ and photoplethysmogram (PPG)) alongside the PSG.

Results

We studied 146 children through the analysis of the SpO₂ pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO₂ variability and SpO₂ spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value ). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO₂ and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO₂ analysis alone.

Conclusions

These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.

Estimating respiratory and heart rates from the correntropy spectral density of the photoplethysmogram

The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.