Obstructive Sleep Apnea Classification Research Articles

Classification of Childhood Obstructive Sleep Apnea based on X-ray images analysis by Quasi-conformal Geometry

Craniofacial profile is one of the anatomical causes of obstructive sleep apnea (OSA). By medical research, cephalometry provides information on patients’ skeletal structures and soft tissues. In this work, a novel approach to cephalometric analysis using quasi-conformal geometry based local deformation information was proposed for OSA classification. Our study was a retrospective analysis based on 60 case-control pairs with accessible lateral cephalometry and polysomnography (PSG) data (mean age: 8.9 ± 2.3 years). By using the quasi-conformal geometry to study the local deformation around 15 landmark points, and combining the results with three linear distances between landmark points, a total of 1218 information features were obtained per subject. A L2 norm based classification model was built. Under experiments, our proposed model achieves high testing accuracy. The accurate classification tool provides us with a reliable and accurate screening tool for childhood OSA.

Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals

Obstructive sleep apnea (OSA) is a disorder which blocks the upper airway during sleep. The severity of OSA will lead heart attack, stroke and end of life. This proposed study explored the classification of OSA and healthy subjects using brain connectivity analysis from electroencephalogram (EEG) signals. Institute of System and Robotics—University of Coimbra (ISRUC) database were used for acquiring 50 EEG signals using 4 channels and noise removal has been accomplished by 50 Hz notch filter. The Institute of System and Robotics—University of Coimbra (ISRUC) database contained 50 EEG signals, with four channels, and a 50 Hz notch filter was applied to remove noise. Wavelet packet decomposition method was performing the segregation of EEG signals into five bands; Gamma (γ), beta (β), alpha (α), theta (θ) and delta (δ). A total of 4 electrode positions were used for the brain connectivity analysis for each EEG band. Pearson correlation method was effectively used for measuring the correlation between healthy and OSA subjects. The nodes and edges were highlighted the connection between brain and subjects. The highest correlation was achieved in delta band of OSA subjects which starts from 0.7331 to 0.9172 respectively. For healthy subjects, the positive correlation achieved was 0.6995. The delta band has been correlated well with brain when compared other bands. It has been noted that the positive correlation well associated with brain in OSA subjects, which classifies OSA from healthy subjects.

Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography Signals.

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model's exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis.

Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity

Background/Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening. MethodsAn Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data. ResultsUsing RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants. ConclusionsThe proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.

An Artificial Intelligence-Based Approach for Automated Classification of Obstructive Sleep Apnea by Considering Multi-modal Feature Fusion Technique

An Artificial Intelligence-Based Approach for Automated Classification of Obstructive Sleep Apnea by Considering Multi-modal Feature Fusion Technique

Multi-Omics Analysis of Circulating Exosomes in Adherent Long-Term Treated OSA Patients.

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease affecting nearly a billion people globally and increasing the risk of multi-organ morbidity and overall mortality. However, the mechanisms underlying such adverse outcomes remain incompletely delineated. Extracellular vesicles (exosomes) are secreted by most cells, are involved in both proximal and long-distance intercellular communication, and contribute toward homeostasis under physiological conditions. A multi-omics integrative assessment of plasma-derived exosomes from adult OSA patients prior to and after 1-year adherent CPAP treatment is lacking. We conducted multi-omic integrative assessments of plasma-derived exosomes from adult OSA patients prior to and following 1-year adherent CPAP treatment to identify potential specific disease candidates. Fasting morning plasma exosomes isolated from 12 adult patients with polysomnographically-diagnosed OSA were analyzed before and after 12 months of adherent CPAP therapy (mean ≥ 6 h/night) (OSAT). Exosomes were characterized by flow cytometry, transmission electron microscopy, and nanoparticle tracking analysis. Endothelial cell barrier integrity, wound healing, and tube formation were also performed. Multi-omics analysis for exosome cargos was integrated. Exosomes derived from OSAT improved endothelial permeability and dysfunction as well as significant improvement in tube formation compared with OSA. Multi-omic approaches for OSA circulating exosomes included lipidomic, proteomic, and small RNA (miRNAs) assessments. We found 30 differentially expressed proteins (DEPs), 72 lipids (DELs), and 13 miRNAs (DEMs). We found that the cholesterol metabolism (has04979) pathway is associated with lipid classes in OSA patients. Among the 12 subjects of OSA and OSAT, seven subjects had complete comprehensive exosome cargo information including lipids, proteins, and miRNAs. Multi-omic approaches identify potential signature biomarkers in plasma exosomes that are responsive to adherent OSA treatment. These differentially expressed molecules may also play a mechanistic role in OSA-induced morbidities and their reversibility. Our data suggest that a multi-omic integrative approach might be useful in understanding how exosomes function, their origin, and their potential clinical relevance, all of which merit future exploration in the context of relevant phenotypic variance. Developing an integrated molecular classification should lead to improved diagnostic classification, risk stratification, and patient management of OSA by assigning molecular disease-specific therapies.

Can the OSA patient be managed within an ERAS protocol? A narrative review

Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder among surgical patients and is associated with an increased risk of perioperative cardiopulmonary complications leading to increased health expenses. Therefore, preoperative OSA screening should be instituted to inform patients of their increased perioperative risk and measures can be taken in to decrease this risk. Meanwhile ERAS (Enhanced recovery after surgery) guidelines are becoming standard of care and being widely implemented. The purpose of these recovery programs is to standardize medical care, enhance outcomes and decrease health care expenses using evidence-based protocols. According to the current evidence it is unclear if OSA patients could benefit from these protocols. The first part of this narrative review states the current evidence on OSA definition, classification, pathophysiology and risk factors, postoperative complications, endotypes and their relevance for the anesthesiologist. The second part investigated the feasibility of ERAS measures within the OSA-population. Preoperatively there are many parallels to be drawn between the ERAS measures and Society of Anesthesia and Sleep Medicine (SASM) guidelines on OSA management: Risk stratification by OSA-screening, information counselling on the increased perioperative risk and optimization of preoperative health conditions by initiation of CPAP-therapy. Perioperatively the use short acting anesthetics allows for both early mobilization and avoid postoperative respiratory compromise by sustained drug effect. ERAS measures to maintain the patient in the zone of normovolaemia are advantageous for OSA patients as excessive fluid administration can worsen OSA. The use of multimodal analgesia sparing opioids might be the most important element beneficial for OSA patients they are more prone to develop opioid induced respiratory depression (OIRD). As OSA patients are at risk for venous thromboembolism appropriate thromboprophylaxis should be applied. Postoperatively OSA patients should be closely monitored in the prevention of OIRD. In conclusion many ERAS measures seem to be applicable or of benefit for OSA patients. However, large prospective randomized controlled trials are needed to evaluate if these patients can be managed within an ERAS protocol.

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.

Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.

The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combination of simple clinical information and imaging recognition based on facial photos may be a useful toolto screen for OSA. We recruited consecutivesubjects suspected of OSA who had received sleep examination and photographing. Sixty-eight points from 2-dimensional facial photos were labelled by automated identification. An optimized model with facial features and basic clinical information was established and tenfold cross-validation was performed. Area under the receiver operating characteristic curve (AUC) indicated the model's performance using sleep monitoring as the reference standard. A total of 653 subjects (77.2% males, 55.3% OSA) were analyzed. CATBOOST was the most suitable algorithm for OSA classification with a sensitivity, specificity, accuracy, and AUC of 0.75, 0.66, 0.71, and 0.76 respectively (P < 0.05), which was better than STOP-Bang questionnaire, NoSAS scores, and Epworth scale. Witnessed apnea by sleep partner was the most powerful variable, followed by body mass index, neck circumference, facial parameters, and hypertension. The model's performance became more robust with a sensitivity of 0.94, for patients with frequent supine sleep apnea. Thefindings suggest that craniofacial features extracted from 2-dimensional frontal photos, especially in the mandibular segment, have the potential to become predictors of OSA in the Chinese population. Machine learning-derived automatic recognition may facilitate the self-help screening for OSA in a quick, radiation-free, and repeatable manner.

A deep neural network-based model for OSA severity classification using unsegmented peripheral oxygen saturation signals

Obstructive sleep apnea (OSA) is a common type of sleep-related breathing disorder, and polysomnography (PSG) remains the gold standard for its diagnosis. However, it takes a significant amount of time to perform PSG in a well-equipped laboratory, and patients typically have to wait a long time for a PSG test. In view of this, over recent years portable and even wearable tools for OSA classification have been developed as a low-cost and easy-to-use alternative to PSG. In this paper, a deep neural network (DNN)-based model was developed to classify OSA severity using peripheral oxygen saturation (SpO2) signals; it showed the following advantages. First, the presented model takes unsegmented SpO2 signals recorded overnight as its input, and OSA severity is then classified as one of four levels as the output. Consequently, there is obviously no need to label segmented signals, and the tremendous amount of effort spent on signal segmentation and then annotation can be completely saved. Second, a high generalization ability is provided since the largest amount of data were used to test the model. This feature gives the model an improved reliability for clinical use. Notably, the outperformance of this work is highlighted in a two-level classification case (with a cutoff apnea–hypopnea index of 5), where the accuracy and the sensitivity increased to above 91% and 95%, respectively.

A Signal Segmentation‐Free Model for Electrocardiogram‐Based Obstructive Sleep Apnea Severity Classification

Obstructive sleep apnea (OSA) has been a common sleep disorder for years, and polysomnography (PSG) remains the gold standard for diagnosing OSA. Nevertheless, PSG is a time and money consuming test, and patients have to wait long for arranging a PSG test in a hospital. In light of this, portable and wearable tools for OSA classification have been developed recently as a low‐cost and easy‐to‐use screening method before undergoing PSG. Using unsegmented electrocardiogram (ECG) signals, a deep neural network (DNN)‐based model is developed here to categorize OSA severity with the following features. First, the model takes unsegmented ECG signals recorded overnight as input, and then generates a four‐level scale as output. Since all the input ECG signals are unsegmented, the tremendous amount of effort spent on signal annotation can be fully saved. Second, the largest amount of data is used to test the model and consequently provide a high generalization ability, as compared with others in the literature. The overall outperformance of this work is highlighted at the end of this article, and this work is validated as an easy‐to‐use and effective screening tool for OSA accordingly.

A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment.

Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed system is dedicated to mapping the pathogenic characteristics of OSA to dynamic changes in blood oxygen concentration and to constructing an automatic approach for assessing OSA. An algorithm including feature extraction, feature selection, and classification is proposed to signals. Permutation entropy(PE), for quantitative measuring the complexity of time series, is firstly involved to characterize the features of the physiological signals. Subsequently, the principal component analysis (PCA) for feature dimensionality reduction and support vector machine (SVM) algorithm for OSA classification are applied. The proposed method has been validated on a dataset that collected by the wearable system. It includes 40 subjects and composes of normal, and various severity cessation of breathing (e.g., mild, moderate, and severe). Experimental results exhibit that the proposed system can effectively distinguish OSA and non-OSA subjects, with an accuracy of 91.89%. The proposed system is expected to pave the novel perspective for OSA assessment in terms of cerebral hemodynamics.

Evaluation of obstructive sleep apnea: an analysis based on aberrant genes.

Obstructive sleep apnea (OSA) is an upstream disorder that frequently causes multisystem disorders. Much research has revealed the pathogenesis of OSA, but there is still a lack of research on the complications caused by OSA. The mRNA expression and methylation dataset based on peripheral blood mononuclear cells (PBMCs) were downloaded from the Gene Expression Omnibus (GEO) database. All differential expressed genes (DEGs) were ranked using the Robust Rank Aggregation (RRA) algorithm. A weighted gene co-expression network analysis (WGCNA) was constructed. Subsequently, we used immune infiltration, enrichment analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis for apnea and hypopnea index (AHI) and hypertension and excessive daytime sleepiness (EDS) and constructed diagnostic model using random forest algorithm. In the present study, we identified 318 DEGs in PBMCs involved in pathogenesis or continuous positive airway pressure (CPAP) therapy. Pathway enrichment identified DEGs associated with protein regulation and metabolism. Notably, through intra group analysis, we found that the immune disorder was more significant for OSAin males, non-daytime sleepy, or non-hypertensive OSA. The area under the ROC curve of model for EDS prediction is 0.889 and 0.852 for hypertension. Notably, we found that the diagnostic model had a high linear predictive value for AHI. Our results indicate that PBMCs are a significant component of alterations in OSA and are expected to explain the mechanism of multisystem diseases caused by OSA. The present study provides new insights for symptom evaluation, classification and treatment of OSA from the molecular level.

The Effects of Barbed Repositioning Pharyngoplasty in Positional and Non-Positional OSA Patients: A Retrospective Analysis.

The aim of our retrospective study is evaluating the effectiveness of barbed repositioning pharyngoplasty (BRP) in a consecutive cohort of patients and assessing its impact on positional indexes in order to potentially identify specific obstructive sleep apnea (OSA) phenotypes for patients who might benefit more significantly from this intervention. A single-center retrospective study with baseline and follow-up type III sleep tests evaluating the Apnea Hypopnea Index (AHI), supine AHI, non-supine AHI, oxygen desaturation index (ODI), mean SaO2, percentage of time spent at SaO2 below 90% (CT90), and lowest oxygen saturation (LOS) were performed. The patients were then divided into groups according to Sher's criteria and Amsterdam Positional OSA Classification (APOC). Parametric and non-parametric tests and univariate and multivariate analyses were conducted. The study finally included 47 patients. The statistical analysis showed significant improvement in AHI, supine AHI, non-supine AHI, and ODI after surgery. The linear regression showed that high values of baseline AHI, AHI supine, and AHI non supine predict more significant postoperative reductions in AHI, AHI supine, and AHI non supine, respectively. Therapeutic success was achieved in 22 patients out of 47. The logistic regression did not find any independent risk factors for success. The most significant reduction in AHI, supine AHI, and non-supine AHI was observed in the APOC 3 group while the APOC 1 patients experience a substantially lower improvement. BRP appears to be an effective surgical procedure for the treatment of OSA. The non-positional patients might benefit more from BRP in comparison with positional patients. Moreover, OSA severity should not be considered an absolute contra-indication for this surgical procedure.

Physical activity for obstructive sleep apnea after stroke? A pilot study assessing the contribution of body fluids.

Obstructive sleep apnea (OSA) and physical inactivity are common after stroke. Physical inactivity can lead to/or exacerbate edema following stroke, and the resultant overnight fluid shift may increase the risk of OSA. We aimed to investigate the effect of physical activity on nocturnal rostral fluid shift, sleep pattern, and edematous state of hemiparetic patients. Neck circumference (tape measured) and arms, legs, and trunk fluid volume (bioelectrical impedance spectrum analyzer) were measured before and after 2 polysomnography (PSG) examinations. In the lab, a whole night PSG was performed after the intervention. The intervention consisted of inactivity (lying down and sitting) or activity (standing, performing calf muscle contractions while standing, walking, and climbing stairs) between 13 and 21 h, after therandomization of the participants. With a 7-day interval, participants crossed over to the other group. From 126 eligible participants, 8 with hemiparetic post-first-ever ischemic stroke at the subacute phase were recruited (age: 53.2 ± 16.2; 6 women). Physical activity reduced AHI from 19 to 13n°/h and wake after sleep onset from 76.5 to 60.3 min and increased fluid volume of paretic and non-paretic arms and trunk before sleep compared to inactivity. An acute bout of physical activity reduced OSA classification based on AHI (from moderate to mild) and sleep fragmentation. Our results provide preliminary evidence of a possible link between physical activity in patients after stroke as an intervention to counteract OSA severity and improve sleep.

SNOOZE AND SLUMBER: THE SLEEP APNEA APNEA-HYPOPNEA INDEX (AHI) VS THE EPWORTH SLEEPINESS SCALE (ESS)

SESSION TITLE: Updates on Sleep-Disordered Breathing and Central HypersomniasSESSION TYPE: Original InvestigationsPRESENTED ON: 10/16/2022 10:30 am - 11:30 amPURPOSE: Obstructive sleep apnea (OSA) has a prevalence of approximately one billion people worldwide and is characterized by hypopneas, apneas and repetitive upper airway collapse during sleep. Several risk factors have been associated with this diagnosis including obesity, older age, male gender as well as upper airway and anatomical abnormalities. While the STOPBANG score is available to determine the pretest probability of OSA, there is little data available comparing a patient’s subjective degree of sleepiness to his OSA classification.METHODS: To better determine if there is any identifiable relation between the symptoms reported by a patient to the likelihood of actually having sleep apnea and to what degree, a retrospective analysis was done looking at 1443 patients seen in the Pulmonary and Sleep Clinic at Beaumont Hospital between the years 2014-2019. The Apnea-Hypopnea Index (AHI) was then compared to the Epworth Sleepiness Scale (ESS) score and evaluated using a Pearson correlation.RESULTS: The data was statistically significant and revealed there is no relation between a patient’s objective AHI versus subjective ESS (Pearson correlation= 0.106, p-value= 0.000). This information was further classified into employed versus unemployed and there was again no correlation indicating a higher ESS equates to more severe OSA, irrespective of employment status (employed AHI average = 25.5, employed ESS average = 10.0, unemployed average AHI = 27, unemployed average ESS = 9.97).CONCLUSIONS: Daytime sleepiness, snoring, nocturnal gasping and unrefreshing sleep are all indicators of possible sleep apnea yet based on this study, symptom severity as measured by the ESS does not correlate to actual sleep apnea or help classify the degree (mild, moderate, or severe) of OSA. Furthermore, whether the patient is employed versus unemployed does not lend itself to any correlation when evaluating the aforementioned scores.CLINICAL IMPLICATIONS: Therefore, it is imperative that physicians never underestimate the possibility of a patient having obstructive sleep apnea if he or she has any of the cardinal symptoms, irrespective of symptomatology severity.DISCLOSURES: No relevant relationships by Patrick FrahmNo relevant relationships by Vishal PatelNo relevant relationships by Tanaz Salimnia SESSION TITLE: Updates on Sleep-Disordered Breathing and Central Hypersomnias SESSION TYPE: Original Investigations PRESENTED ON: 10/16/2022 10:30 am - 11:30 am PURPOSE: Obstructive sleep apnea (OSA) has a prevalence of approximately one billion people worldwide and is characterized by hypopneas, apneas and repetitive upper airway collapse during sleep. Several risk factors have been associated with this diagnosis including obesity, older age, male gender as well as upper airway and anatomical abnormalities. While the STOPBANG score is available to determine the pretest probability of OSA, there is little data available comparing a patient’s subjective degree of sleepiness to his OSA classification. METHODS: To better determine if there is any identifiable relation between the symptoms reported by a patient to the likelihood of actually having sleep apnea and to what degree, a retrospective analysis was done looking at 1443 patients seen in the Pulmonary and Sleep Clinic at Beaumont Hospital between the years 2014-2019. The Apnea-Hypopnea Index (AHI) was then compared to the Epworth Sleepiness Scale (ESS) score and evaluated using a Pearson correlation. RESULTS: The data was statistically significant and revealed there is no relation between a patient’s objective AHI versus subjective ESS (Pearson correlation= 0.106, p-value= 0.000). This information was further classified into employed versus unemployed and there was again no correlation indicating a higher ESS equates to more severe OSA, irrespective of employment status (employed AHI average = 25.5, employed ESS average = 10.0, unemployed average AHI = 27, unemployed average ESS = 9.97). CONCLUSIONS: Daytime sleepiness, snoring, nocturnal gasping and unrefreshing sleep are all indicators of possible sleep apnea yet based on this study, symptom severity as measured by the ESS does not correlate to actual sleep apnea or help classify the degree (mild, moderate, or severe) of OSA. Furthermore, whether the patient is employed versus unemployed does not lend itself to any correlation when evaluating the aforementioned scores. CLINICAL IMPLICATIONS: Therefore, it is imperative that physicians never underestimate the possibility of a patient having obstructive sleep apnea if he or she has any of the cardinal symptoms, irrespective of symptomatology severity. DISCLOSURES: No relevant relationships by Patrick Frahm No relevant relationships by Vishal Patel No relevant relationships by Tanaz Salimnia

Use of the Complete Airway Repositioning and Expansion (CARE) approach in 220 patients with Obstructive Sleep Apnea (OSA): A retrospective cohort study

Objective/BackgroundObstructive sleep apnea (OSA) is a prevalent disease with significant health repercussions. While many effective OSA treatment modalities exist, Complete Airway Repositioning and Expansion (CARE) represents an emerging approach that leverages gradual airway expansion, with or without mandibular advancement. We conducted a retrospective study of patients who underwent CARE with a dental provider and examined how their sleep study data changed, with a focus on apnea hypopnea index (AHI). Patients/MethodsA retrospective database of 220 adult patients was examined. Demographic data and radiographic and sleep study data were compared in patients before and following at least 6 months of treatment with one of two possible dental devices. ResultsThe median age of patients in this cohort was 50 years, and evenly split by gender. The median decrease in AHI was 49.0%, with a median pre-treatment AHI of 17.3 and median post-treatment AHI of 9.6 (p<0.001). Most participants (63.6%) demonstrated an improvement in their OSA severity class. Fifty-seven (25.9%) participants had complete resolution of their OSA. Post-treatment, 151 (68.6%) of patients had OSA severities of none or mild. Thirty-four (15.5%) of patients had in increase in AHI and 13 (6.0%) of these patients demonstrated an increase in OSA classification. One patient experienced an adverse event in the form of a loose molar tooth requiring repair. Overall findings were limited by missingness of BMI and clinical co-morbidity data, as well as quality of life measures. ConclusionsIn this large, but data limited retrospective series, CARE seems to be an effective and safe approach to OSA management that may be a useful alternative to current mainstays of OSA management. Further investigation is warranted.

Obstructive Sleep Apnea (OSA) Classification Based on Heart Rate Variability (HRV) on Electrocardiogram (ECG) Signal Using Support Vector Machine (SVM)

Obstructive sleep apnea (OSA) is a disorder that occurs in a person's sleep pattern, and due to its ability to cause other health-related problems, various monitoring methods have been developed. One of the proposed methods is the ECG signal known as ECG-derived respiration (EDR), which uses changes in rhythm and patterns regularity to detect OSA occurrence. Therefore, this study aims to determine the OSA classification based on heart-rate variability (HRV) on electrocardiogram (ECG) signal using a support vector machine (SVM). The HRV parameter displays the rhythmic changes in the ECG signal under normal and OSA conditions. Eleven HRV characteristics were used to produce the highest accuracy of 89.5% with a support vector machine (SVM) as a classifier. The results were tested on a 1 minute long ECG signal annotated by an expert, which indicated that OSA can be detected by observing the dynamics of the distance of the R-R wave on the ECG signal.

Automated sleep apnea classification based on statistical and spectral analysis of electrocardiogram signals

&lt;span&gt;Well-being sleep is a significant segment for maintaining mental comparably as genuine flourishing. More than six-hour recordings are required to distinguish sleep apnea, which are extremely long duration recordings. It's difficult for a human to deduce the problem from electrocardiogram (ECG) readings. As a result, automated PC-based assessment is expected to detect &lt;span&gt;abnormalities as early as possible. An automated framework for the classification of obstructive sleep apnea (OSA) can moreover be distinguished from the ECG Signals. From the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) polysomnographic informational collection, 18 subjects have been considered as data signals. The signal is segmented into 30 seconds and features are extracted by using the discrete wavelet transform (DWT). DWT of seven-level decomposition is applied on the segmented signal by using&lt;/span&gt; the wavelet 'sym3'. 12 features were extracted from each level and all of them are used to categorize the five types of sleep apnea. Random forest, k-nearest neighbor (KNN), and support vector machine (SVM) are used for classification of apnea. The random forest (RF) classifier outperformed the others with an average of accuracy (Acc) of 98.53% according to the study's findings. The experimental results show the developed model outperforms the state of art algorithms in the literature.&lt;/span&gt;

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications.

Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night's sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.