Diagnosis Of Obstructive Sleep Apnea Research Articles

Performance Accuracy of Wrist-Worn Oximetry and Its Automated Output Parameters for Screening Obstructive Sleep Apnea in Children

Objectives: Obstructive sleep apnea (OSA) increases the risk of perioperative adverse events in children. While polysomnography (PSG) remains the reference standard for OSA diagnosis, oximetry is a valuable screening tool. The traditional practice is the manual analysis of desaturation clusters derived from a tabletop device using the McGill oximetry score. However, automated analysis of wearable oximetry data can be an alternative. This study investigated the accuracy of wrist-worn oximetry with automated analysis as a preoperative OSA screening tool. Methods: Healthy children scheduled for adenotonsillectomy underwent concurrent overnight PSG and wrist-worn oximetry. PSG determined the obstructive apnea-hypopnea index (OAHI). Oximetry data were auto-analyzed to determine 3% oxygen desaturation index (ODI3) and visually scored as per McGill criteria. The logistic regression model assessed the predictive performance of ODI3 for detecting the presence and severity of OSA after adjusting for covariates. Results: Seventy-six children (34 females), aged (mean±standard deviation) 5.7±1.6 years were classified, based on PSG-derived OAHI, as no OSA (n=31), mild (n=31), and moderate-severe OSA (n=14). Oximetric ODI3 was identified as the sole predictor of moderate-severe OSA (OAHI≥5 events/h) (odds ratio 1.38, 95% confidence interval 1.15, 1.65, <i>p</i>=0.001). The best diagnostic performance was at ODI3=5 events/h (78.6% sensitivity, 75.8% specificity [receiver operating characteristic-area under the curve {ROC-AUC}=0.857]). ODI3 was also more sensitive than the McGill oximetry score in diagnosing moderate-severe OSA (78.6% by ODI3 vs. 33.0% by McGill). The performance was suboptimal for any level of OSA (OAHI≥1 event/h) (75.6% sensitivity, 61.3% specificity [ROC-AUC=0.709]). Conclusions: Wrist-worn oximetry-derived automated ODI3 can reliably identify moderate-severe OSA in children undergoing adenotonsillectomy, making it a potentially useful preoperative OSA screening tool.

Automatic snoring detection using a hybrid 1D–2D convolutional neural network

Snoring, as a prevalent symptom, seriously interferes with life quality of patients with sleep disordered breathing only (simple snorers), patients with obstructive sleep apnea (OSA) and their bed partners. Researches have shown that snoring could be used for screening and diagnosis of OSA. Therefore, accurate detection of snoring sounds from sleep respiratory audio at night has been one of the most important parts. Considered that the snoring is somewhat dangerously overlooked around the world, an automatic and high-precision snoring detection algorithm is required. In this work, we designed a non-contact data acquire equipment to record nocturnal sleep respiratory audio of subjects in their private bedrooms, and proposed a hybrid convolutional neural network (CNN) model for the automatic snore detection. This model consists of a one-dimensional (1D) CNN processing the original signal and a two-dimensional (2D) CNN representing images mapped by the visibility graph method. In our experiment, our algorithm achieves an average classification accuracy of 89.3%, an average sensitivity of 89.7%, an average specificity of 88.5%, and an average AUC of 0.947, which surpasses some state-of-the-art models trained on our data. In conclusion, our results indicate that the proposed method in this study could be effective and significance for massive screening of OSA patients in daily life. And our work provides an alternative framework for time series analysis.

Obstructive sleep apnea: a follow-up program in its relation to temporomandibular joint disorder, sleep bruxism and orofacial pain

ObjectiveTo evaluate the correlation between obstructive sleep apnea (OSA) and temporomandibular joint (TMJ) morphology, tooth wear condition, orofacial pain through a follow-up program.Materials and methodsSeventy one OSA patients were divided into three groups according to their (apnea hypopnea index) AHI: mild group (n = 23), moderate group (n = 24), and severe group (n = 24). All patients had OSA therapies around six months after confirm the diagnosis of OSA. The tooth wear score and orofacial pain condition of all patients were recorded via clinical examination. Cone beam computed tomography (CBCT) images were also taken when confirm the diagnosis of OSA (T0), 6 months after the diagnosis (T1), and 6 months after the OSA treatment (T2). Parameters indicating the condylar morphology and joint space were evaluated. The differences of clinical symptoms and TMJ conditions among T0, T1 and T2 time point were detected in the three groups respectively. The changes in T1-T0 and T2-T1 of all descriptions among three groups were also compared. The correlations between AHI and clinical symptoms were detected with Spearman correlation analysis.ResultsIn mild group, there was no difference in all clinical symptoms and TMJ morphology among the three time points. Both in moderate and severe group, the condylar volume, superficial area, wear score, visual analogue scales (VAS), and R value (indicating condyle position) displayed significant differences among the three time points (P < 0.05). From T0 to T1, mild group displayed fewer decreases in the condylar volume and superficial area and fewer increases in wear score than that in moderate and severe group (P < 0.05). From T1 to T2, there was a greatest reduction in severe group for R value, and significant difference in the description of VAS and R value were found among the three groups. AHI was negatively correlated condylar volume and condylar superficial area, and was positively correlated with tooth wear score and VAS (P < 0.05).ConclusionModerate to severe OSA will aggravate orofacial pain and tooth wear, affect TMJ volume and superficial area, even change the location of condyles. Appropriate OSA therapies may be effective ways to alleviate these adverse effects in long-term.

The role of the WatchPAT device in the diagnosis and management of obstructive sleep apnea

Obstructive sleep apnea (OSA) is a common condition affecting an estimated 936 million individuals worldwide, leading to a considerable demand for diagnostic services. Polysomnography, the current gold standard for diagnosis, is resource intensive and inconvenient for patients and healthcare providers. The WatchPAT is an unobtrusive device for home OSA diagnosis. It utilizes peripheral arterial tomography in conjunction with heart rate, oximetry, actingraphy, and respiratory movements for the diagnosis of OSA. It has good correlation with polysomnography for OSA diagnosis and also reports sleep time and sleep staging. The WatchPAT device has reported sensitivities of 81–95%, specificities of 66–100%, positive predictive values of 79–96%, and negative predictive values of 92% for the determination of the apnea–hypopnea index (AHI). It has also been studied and its use validated in a variety of patient populations, including children, older adults, pregnant women, and those with comorbid medical conditions. The device has also been adopted for use in screening for cardiac arrhythmia and central sleep apnea, although neither use has become widespread. With the emergence of telemedicine and an increasing demand for sleep services, the WatchPAT device can be a useful aid in OSA diagnostics.

“Something is wrong!” A qualitative study of racial disparities in parental experiences of OSA detection in their child

IntroductionApproximately 3% of American children are affected by obstructive sleep apnea (OSA), yet Black children are 2–4 times more likely to experience OSA compared to White children. Little is known about parental experiences in detection, diagnosis, and treatment of OSA in their child, and how these experiences may differ by race. The study objective was to highlight convergent and divergent experiences between and across Black and White parents in the OSA detection process for their child.MethodsWe conducted 27 semi-structured interviews with mothers whose child was referred for a diagnostic overnight polysomnogram (PSG) to assess for OSA. Parents described how their child was referred for a PSG and their perceptions and feelings throughout the detection process. Data were analyzed using a thematic descriptive approach. Frequency of themes were examined by race. Themes that were unique to one racial group were categorized as divergent, whereas themes described by individuals from both groups were categorized as convergent. Within the convergent themes, we examined the prevalence within each racial group, noting those that were more prevalent (&amp;gt;10% difference in prevalence) in one race or the other.ResultsThe sample included 19 Black and 8 White mothers, who were 36 years old on average. Qualitative analysis yielded 21 themes across 5 categories that captured divergent and convergent experiences across Black and White mothers during the OSA detection process for their child. Divergent themes that were unique to Black mothers included It Takes a Village—Teacher, Misplaced Blame, Missing the Day/night Connection, Trust in Provider, and the belief that Snoring is Normal. Only one divergent theme among White parents emerged, worries about Dying in Ones Sleep. Additional convergent themes were identified that were more prevalent in one race compared to the other.DiscussionBlack and White mothers experienced different paths to detection and diagnosis for their child's sleep disordered breathing, that are affected by individual awareness, education, patient-provider interactions, and experiences with the healthcare system. Divergent themes such as Misplaced Blame among Black mothers were a potential indication of racism and health disparities.

Upper Airway Characteristics and Morphological Changes by Different MADs in OSA Adult Subjects Assessed by CBCT 3D Imaging.

This study aimed to analyse the effectiveness of four different designs of the Mandibular Advancement Device (MAD) and the morphological changes on upper airway characteristics of Obstructive Sleep Apnea (OSA) patients inducted by each of them, detected by Cone Beam Computer Tomography (CBCT) 3D imaging. Twenty-two patients were recruited after an OSA diagnosis with PSG. Four different customised and titratable MADs were used and an initial CBCT scan was obtained for each patient. Six months after the end of the MAD titration phase, all the subjects performed a second PSG with the MAD in situ; the second PSG showed an Apnoea-Hypopnoea Index (AHI) of <5 or a decrease of 50% in AHI when compared with the initial AHI. Moreover, a second CBCT scan with the MAD in situ was performed. DICOM files were imported into the airway analysis software programme and the pharyngeal area around the oropharynx was highlighted. The area and volume of the oropharynx with and without the device was evaluated. A considerable improvement of the airway was observed (+33.76%), and a significant difference in the enlargement ratio between the posterior soft palate (+32.41%) and the posterior tongue (+36.96%) region was also found. The greatest increase in airway volume was achieved in patients treated with the MAD Forward and TAP (+42.77% and +41.63%, respectively). MAD therapy is effective to treat moderate to severe OSA with an increased upper airway volume. The design of the MAD can influence the effectiveness of the treatment.

Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry

Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark.

Health disparities in the detection and prevalence of pediatric obstructive sleep apnea

IntroductionPediatric Obstructive Sleep Apnea (OSA) is associated with negative health outcomes, behavior problems, and poor academic performance when left untreated. Prior research has shown that children from racial and ethnic minority backgrounds and those living in lower socioeconomic status (SES) homes or neighborhoods have higher prevalence of OSA but lower likelihood of receiving evidence-based care for OSA. Disparities in pediatric OSA detection (e.g., timely assessment and diagnosis) likely contribute to this observed inequity in receiving treatment. A polysomnogram (PSG) is the gold standard for diagnosing OSA but completing PSG can be challenging. Study aims were to examine racial, ethnic, and SES differences in (1) OSA prevalence and severity and (2) OSA detection, specifically PSG completion rates, timing since referral, and age of diagnosis.MethodsChildren (aged 1–18, N = 1,860, 56% male) were referred for PSG during a 6-month period. Participants' racial/ethnic background were as follows: 64.8% White non-Hispanic, 23.5% Black non-Hispanic, 9.4% White Hispanic, and 2.4% other. Children predominantly had Medicaid insurance (64.5%). SES was measured by insurance type and neighborhood SES using the Distressed Communities Index (DCI) for each participant's zip code (Economic Innovations Group; https://eig.org/dc). Covariates included child age and sex, BMI, premature birth status, and smoke exposure in the home.ResultsWe replicated previous research by showing that children from minority racial/ethnic backgrounds and lower SES backgrounds had higher prevalence rates of OSA and worse disease severity. Across racial, ethnic, and socioeconomic backgrounds, only 31.6% of the children referred successfully completed PSG. Insurance coverage (Medicaid or private vs. self-pay), was an important factor in predicting earlier timing and better completion rates of PSG, which is essential for successful diagnosis and treatment of pediatric OSA.

Effects of obstructive sleep apnea on myocardial injury and dysfunction: a review focused on the molecular mechanisms of intermittent hypoxia.

Obstructive sleep apnea (OSA) is characterized by intermittent hypoxia (IH) and is strongly associated with adverse cardiovascular outcomes. Myocardial injury and dysfunction have been commonly observed in clinical practice, particularly in patients with severe OSA. However, the underlying mechanisms remain obscure. In this review, we summarized the molecular mechanisms by which IH impact on myocardial injury and dysfunction. In brief, IH-induced cardiomyocyte death proceeds through the regulation of multiple biological processes, including differentially expressed transcription factors, alternative epigenetic programs, and altered post-translational modification. Besides cell death, various cardiomyocyte injuries, such as endoplasmic reticulum stress, occurs with IH. In addition to the direct effects on cardiomyocytes, IH has been found to deteriorate myocardial blood and energy supply by affecting the microvascular structure and disrupting glucose and lipid metabolism. For better diagnosis and treatment of OSA, further studies on the molecular mechanisms of IH-induced myocardial injury and dysfunction are essential.

Obesity paradox or hypoxia preconditioning: How obstructive sleep apnea modifies the Obesity-MI relationship

ObjectivesThe objective of this study was to evaluate the interaction between obesity and obstructive sleep apnea on acute MI in hospital mortality. MethodsThis retrospective cohort study utilized Veterans Health Administration data from years 1999–2020. Participants were categorized according to their body mass index (BMI) to non-obese (BMI <30) and obese (BMI ≥30) groups. Clinical obstructive sleep apnea (SA) diagnosis was confirmed using ICD9/10 codes and the study subgroups included non-obese with no obstructive sleep apnea (nOB-nSA), non-Obese with obstructive sleep apnea (nOB-SA), obese with no obstructive sleep apnea (OB-nSA), and obese with obstructive sleep apnea (OB-SA). The primary outcome was odds ratio of in-hospital mortality during the hospitalization with acute MI as the principal diagnosis adjusted for age, gender, race, ethnicity, and Charlson comorbidity index (CCI) with the nOB-nSA group as the comparison group. ResultsAmong 72,036 veterans with acute-MI hospitalization, individuals with obesity and obstructive sleep apnea (OB-SA) had the lowest in-hospital mortality rate (1.0%) compared to those without obesity and obstructive sleep apnea (nOB-nSA, 2.8%), with obesity but without obstructive sleep apnea (OB-nSA, 2.4%), and with obesity and obstructive sleep apnea (nOB-SA, 1.4%). The adjusted odds ratio for mortality, compared to nOB-nSA, was 9% higher but not significant in OB-nSA (aOR, 1.09, 95%CI: 0.95, 1.25), 46% lower in OB-nSA (aOR, 0.54, 95%CI: 0.45, 0.66), and 52% lower in OB-SA (aOR, 0.48: 95%CI: 0.41, 0.57). ConclusionOur data suggest that the association between obesity and improved survival in acute MI is largely driven by the presence of sleep apnea.

Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) is the most common type of sleep-disordered breathing. This article describes advances in the diagnosis, testing, treatment, and monitoring of OSA. Home sleep apnea testing and in-laboratory polysomnography are the most commonly used diagnostic tools in the identification and monitoring of OSA, but new methods for diagnosis and at-home monitoring of treatment response are being developed and validated. While the apnea-hypopnea index is regularly used to define OSA severity, recognition is increasing of its inability to risk-stratify patients. Other sleep study data including arousal threshold, hypoxic burden, and pulse rate variability as well as clinical characteristics can help with risk stratification. The most effective treatment is continuous positive airway pressure (CPAP), which can be limited by adherence and tolerance in some patients. Newer masks and comfort features including heated tubing and expiratory pressure relief may improve tolerance to positive airway pressure (PAP) therapy. Additional treatment options include other PAP modalities, mandibular advancement devices, tongue stimulation therapy, negative inspiratory pressure, nasal expiratory pressure valves, nasal congestion treatments, upper airway surgeries including hypoglossal nerve stimulation, and medications. OSA is a common disorder that causes sleep and daytime symptoms and increases the risk of neurologic and medical complications. Neurologists should be aware of atypical presentations and understand the diagnostic and treatment options.

Factors associated with the diagnosis and treatment of obstructive sleep apnoea in chronic heart failure: a mixed methods study

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): This study was co-funded by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care East Midlands (CLAHRC EM), now recommissioned as NIHR Applied Research Collaboration East Midlands (ARC EM) and Leicestershire Partnership NHS Trust - Raising Health Charitable Funds Award and supported by the NIHR Leicester Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Background Heart Failure (HF) is associated with a considerable burden of disability that includes frequent hospitalisation and high risk of mortality. Despite advances in HF treatment, many patients continue to experience symptoms and will die from progressive HF, often exacerbated by the presence of co-morbidities, such as obstructive sleep apnea (OSA). Despite the high prevalence of OSA in chronic HF and the association with the development and progression of chronic HF, OSA remains largely under diagnosed and under treated. Purpose To explore factors associated with the diagnosis and treatment of OSA in CHF. Methods A convergent parallel mixed methods research design was utilised. Quantitative methods included a systematic review and meta-analysis, diagnostic validation study and a survey study, whilst semi-structured interviews and reflexive thematic analysis was utilised for the qualitative component. Data collection and data analysis were conducted simultaneously with the integration of the findings in the discussion. Results The systematic review and meta-analysis showed that the STOP-Bang screening questionnaire had a high sensitivity to detect OSA in the sleep clinic and surgical populations but lacked adequate specificity. The diagnostic validation study indicated a high sensitivity of the STOP-Bang questionnaire, but due to differences in prevalence, risk of selection bias and spectrum effect, validation in a sleep clinic population is unlikely to reflect the true performance of the STOP-Bang questionnaire in a stable CHF population. The survey study demonstrated a knowledge deficit in the diagnosis and treatment of OSA, and variable practice among HF clinicians in the UK. Findings from the qualitative component identified several key barriers and enablers across the OSA diagnostic pathway from both patients’ and clinicians’ accounts. Conclusion Due to a lack of typical OSA symptoms, there is currently no real evidence of cardiovascular hospitalisation and mortality benefit for OSA treatment in CHF. This contributed to a paucity of guidance on the management of OSA in CHF clinical guidelines. Due to a lack of typical OSA symptoms, screening questionnaires that rely on patient symptoms may be less effective. Although clinicians felt that OSA was an important clinical condition, findings demonstrated a disconnect between the stated level of clinical importance and clinicians’ actual level of knowledge. The current absence of evidence to support the treatment of OSA in CHF and paucity of CHF guidelines to inform the management of OSA in CHF, are likely to contribute to clinicians not prioritising the recognition and treatment of OSA in CHF.

Selenoprotein P, peroxiredoxin-5, renalase, and total antioxidant status in patients with suspected obstructive sleep apnea

PurposeThe aim of this study was to investigate the relationship between selenoprotein P, peroxiredoxin-5, renalase, total antioxidant status (TAS), mean blood pressure (mBP), and apnea-hypopnea index (AHI).MethodsThe study group consisted of 112 patients hospitalized to verify the diagnosis of obstructive sleep apnea (OSA). The inclusion criteria were consent to participate in the study and age ≥ 18 years. Patients with active proliferative disease, severe systemic diseases, or mental diseases were excluded from the study. Each patient underwent full polysomnography and had blood pressure measured. Blood samples were collected and laboratory test was performed.ResultsAmong 112 patients enrolled, there was a statistically significant negative linear correlation between blood pressure values (sBP, dBP, mBP) and selenoprotein P, renalase, and TAS levels. Similarly, there was a negative linear correlation between AHI and selenoprotein P, renalase, and TAS levels, but none between AHI and peroxiredoxin-5. Based on the obtained regression models, higher selenoprotein P, peroxiredoxin-5, and renalase levels were independently associated with higher TAS. Lower mBP values were independently associated with the use of antihypertensive drugs, higher TAS, and younger age. Male gender, higher BMI, and higher mBP were independently associated with higher AHI.ConclusionsHigher concentrations of selenoprotein P, peroxiredoxin-5, and renalase were associated with higher TAS, which confirms their antioxidant properties. There was an indirect connection between tested antioxidants and blood pressure values.

Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea.

Topological data analysis (TDA) is an emerging technique for biological signal processing. TDA leverages the invariant topological features of signals in a metric space for robust analysis of signals even in the presence of noise. In this paper, we leverage TDA on brain connectivity networks derived from electroencephalogram (EEG) signals to identify statistical differences between pediatric patients with obstructive sleep apnea (OSA) and pediatric patients without OSA. We leverage a large corpus of data, and show that TDA enables us to see a statistical difference between the brain dynamics of the two groups.Clinical relevance- This establishes the potential of topological data analysis as a tool to identify obstructive sleep apnea without requiring a full polysomnogram study, and provides an initial investigation towards easier and more scalable obstructive sleep apnea diagnosis.

Craniofacial risk factors for obstructive sleep apnea-systematic review and meta-analysis.

Obstructive sleep apnea (OSA) is caused by temporary partial or complete constriction of the upper airway during sleep which leads to reduced blood oxygen and cardiovascular risks. Main symptoms vary between adults and children leading to misdiagnosis or delayed patient identification. To improve early diagnosis, lateral cephalograms can provide craniofacial measurements associated with a higher risk of OSA. In order to identify the most relevant craniofacial measurements, a systematic literature review with meta-analysis was conducted combining the terms 'orthodontic*', 'craniofacial', 'cephalometr*', 'cephalogram', 'OSA*', 'UARS', 'SDB', 'sleep disordered breathing', 'sleep apnea' and 'sleep apnoea'. Of 3016 publications, 19 were included in the systematic review and meta-analysis, 15 with adult patients and four with children. A total of 16 measurements (six angles, 10 distances) were compared, nine showed a possible influence in patients with OSA compared to controls: NSBa angle (-0.28°), ANB angle (+0.33°), ML-NSL angle (+0.34°), Me-Go-Ar angle (+0.33°), SN distance (-0.70 mm), N-ANS distance (-0.36 mm), MP-H distance (+1.18 mm), uvula length (+1.07 mm) and thickness (+0.96 mm). Posterior airway measurements were not sufficiently described or comparably measured to be statistically analysed. There is some evidence for altered craniofacial anatomy in patients with OSA compared to controls. Lateral cephalograms should be screened for these aspects routinely to improve early diagnosis of OSA and craniofacial orthopaedics should complement the interdisciplinary treatment plan for young patients with OSA.

Peripheral arterial tonometry versus polysomnography in suspected obstructive sleep apnoea

BackgroundPolysomnography (PSG) is the gold standard for the diagnosis of obstructive sleep apnoea (OSA). Home sleep apnoea testing with peripheral arterial tonometry (PAT) is a recommended diagnostic alternative for patients with an increased risk for OSA.In a large clinical cohort, we investigated concordance and predictors for discordance in diagnosing OSA using PAT and PSG, and three-year cardiovascular risk in patients with discordant OSA diagnosis.MethodsRetrospective monocentric cohort study. Patients with a PAT AHI ≥ 5/h followed by an in-hospital PSG within three months were included. All patients with a PAT AHI ≥ 5/h but a PSG AHI < 5/h were classified as discordant. Patients with PAT and PSG AHI ≥ 5/h were classified as concordant. To ascertain cardiovascular risk, major adverse cardiovascular events (MACE) were analyzed in discordant patients and sex, age, body mass index (BMI) and cardiovascular disease-matched concordant patients over a follow-up time of 3.1 ± 0.06 years.ResultsA total of 940 patients, 66% male with an average age of 55 ± 0.4 years and BMI of 31 ± 0.2 kg/m2 were included. Agreement in OSA diagnosis was observed in 80% of patients (55% in mild and 86% in moderate and severe OSA). Factors significantly associated with a discordant diagnosis were female sex, younger age and lower BMI, but not comorbidities. There was no significant difference in MACE (p = 0.920) between discordant patients (n = 155) and matched concordant patients (n = 274) with or without therapy.ConclusionsConcordance between PAT and PSG diagnosis of sleep apnoea is good, particularly in moderate and severe OSA. Predictors for discordant results between PAT and PSG were age, sex and BMI. MACE risk is similar in those with OSA diagnosed by PAT or PSG.

Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3-7% of males and 2-5% of females. In the United States alone, 50-70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis.

Investigating associations between social determinants, self-efficacy measurement of sleep apnea and CPAP adherence: the SEMSA study.

The prospective Self-Efficacy Measure for Sleep Apnea study (SEMSAS) is investigating thresholds for health literacy, self-efficacy and precariousness at obstructive sleep apnea (OSA) diagnosis to predict CPAP adherence. This paper describes the study protocol and presents baseline data from the ongoing study. Eligible individuals had confirmed OSA and were referred to a homecare provider for continuous positive airway pressure (CPAP) therapy initiation. Data on patient characteristics and comorbidities were collected, along with baseline evaluations of self-efficacy [15-item Self-Efficacy Measure for Sleep Apnea tool (SEMSA-15)], precariousness [Deprivation in Primary Care Questionnaire (DipCareQ)], and health literacy (Health Literacy Questionnaire). CPAP adherence over 12 months of follow-up will be determined using remote monitoring of CPAP device data. The primary objective is to define an optimal SEMSA-15 score threshold to predict CPAP adherence at 3- and 12-month follow-up. Enrollment of 302 participants (71% male, median age 55 years, median body mass index 31.6 kg/m2) is complete. Low self-efficacy (SEMSA-15 score ≤ 2.78) was found in 93/302 participants (31%), and 38 (12.6%) reported precariousness (DipCareQ score > 1); precariousness did not differ significantly between individuals with a SEMSA-15 score ≤ 2.78 versus >2.78. Health literacy was generally good, but was significantly lower in individuals with versus without precariousness, and with low versus high self-efficacy. SEMSAS is the first study using multidimensional baseline assessment of self-efficacy, health literacy and precariousness, plus other characteristics, to determine future adherence to CPAP, including CPAP adherence trajectories. Collection of follow-up data is underway.

Development of New Machine Learning Based Algorithm for the Diagnosis of Obstructive Sleep Apnea from ECG Data

In this study, a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea (OSA) from the analysis of single-channel ECG recordings. Eighteen ECG recordings from the PhysioNet Apnea-ECG dataset were used in the study. In the feature extraction stage, dynamic time warping and median frequency features were obtained from the coefficients obtained from different frequency bands of the ECG data by using the wavelet transform-based algorithm. In the classification phase, OSA patients and normal ECG recordings were classified using Random Forest (RF) and Long Short-Term Memory (LSTM) classifier algorithms. The performance of the classifiers was evaluated as 90% training and 10% testing. According to this evaluation, the accuracy of the RF classifier was 82.43% and the accuracy of the LSTM classifier was 77.60%. Considering the results obtained, it is thought that it may be possible to use the proposed features and classifier algorithms in OSA classification and maybe a different alternative to existing machine learning methods. The proposed method and the feature set used are promising because they can be implemented effectively thanks to low computing overhead. 

Sociodemographic disparities and healthcare utilization in pediatric obstructive sleep apnea management

ObjectivesWe examined (1) disparities in obstructive sleep apnea (OSA) care by insurance coverage, and by child race and ethnicity among Medicaid-insured children (MIC), and (2) healthcare utilization changes after OSA care. MethodsIBM MarketScan insurance claims were used to index OSA care 1-year before and after initial OSA diagnosis in 2017 among 2-17-year-old children (n = 31,787, MIC: 59%). OSA care and healthcare utilization analyses adjusted for child age, sex, obesity, and complex chronic conditions. ResultsWe identified 8 OSA care pathways, including no care, which occurred in 34.4% of the overall sample. MIC had 13% higher odds of no OSA care compared to commercially-insured children (CIC). MIC had 32–48% lower odds of any treatment pathway involving specialty care, but a 13–46% higher likelihood of receiving surgical care without polysomnogram (PSG) and PSG only. In MIC, non-Latinx Black/African American (Black) and Hispanic/Latinx children were 1.3–2.2 times more likely than White children to receive treatment involving specialty care and/or PSG, while Black children were 31% less likely than White youth to undergo surgery. In the full sample, surgical care was associated with less outpatient and emergency healthcare utilization compared to those untreated or not surgically treated. ConclusionsVaried OSA management by insurance coverage suggests disparities in access to and engagement in care and potentially greater disease burden among MIC. Surgical care is associated with reduced healthcare utilization. The lower odds of surgery in Black MIC should be further evaluated in the context of OSA severity, healthcare biases, and family preferences.