Detection Of Obstructive Sleep Apnea Syndrome Research Articles

Barriers and facilitators to detection and treatment of obstructive sleep apnoea syndrome in people with severe mental illnesses, qualitative interview study and referrer survey

Obstructive sleep apnoea syndrome (OSAS) is a relatively common condition. It has a range of negative health and quality of life implications, but there are established tests to detect it, and recommended treatment pathways which have been well tested. OSAS is prevalent in people with severe mental illness (SMI), and research suggests it is under-detected in this group. This study aimed to examine and explore barriers and facilitators to the detection and treatment of OSAS in people with severe mental illnesses. Staff and patients with a range of relevant experiences were interviewed, and a large scale survey of potential OSAS referrers was conducted. Interviews were analysed thematically, survey responses were summarised descriptively, and results are presented together for comparison and enhanced interpretation. The results highlight a lack of awareness of OSAS outside of specialist sleep services, resulting in poor detection and treatment of OSAS in people with SMI. Comorbidities, symptoms and circumstances of this group may make the presence of OSAS less obvious, and can make diagnosis and adapting to treatment more difficult, but also emphasise the benefits of successful continuous positive airway pressure (CPAP) treatment in this group. The challenges of adapting to CPAP were described and acknowledged, but the impact of successful CPAP was for some life changing. The harm of delayed diagnosis was perceived as unjust. We recommend increased screening for OSAS in people with SMI, supported by increased education and training about OSAS in staff who work with people with severe mental illnesses, and increased awareness in service users and carers.

Determination of Signs of Sleep Apnea Using Machine Learning Methods in Combination with Reducing the Dimensionality of Heart Rate Variability Features

Obstructive Sleep Apnea Syndrome (OSAS) is a clinically significant disorder characterized by recurrent episodes of upper airway obstruction, manifesting as either apnea or hypopnea, predominantly occurring at the pharyngeal level. Despite the preservation of respiratory muscle function during these episodes, OSAS poses considerable health risks, including cardiovascular complications and cognitive impairment. In recent years, a growing body of literature has explored novel methodologies to discern and diagnose OSAS, with a particular focus on cardiac activity analysis through Heart Rate Variability (HRV). This study contributes to the existing literature by conducting a comprehensive HRV analysis aimed at identifying indicative patterns of sleep apnea. The analysis incorporates diverse parameters within both time and frequency domains, facilitating a nuanced understanding of the complex interplay between cardiac dynamics and respiratory disruptions during sleep. In an effort to enhance the interpretability of the data, various scaling and dimensionality reduction techniques, such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), were applied. The dataset utilized in this investigation comprises records from 70 patients, sourced from the Apnea-ECG Database on the Physionet platform. To discern the optimal classification model, several machine learning algorithms were employed after the dimensionality reduction, including k-Nearest Neighbors (k-NN), logistic regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting. Intriguingly, the results demonstrate a remarkable 100% accuracy across all classifiers when utilizing the UMAP dimensionality reduction method. A distinctive feature of the proposed methodology lies in its amalgamation of machine learning techniques with HRV parameters post-dimensionality reduction. This approach not only enhances the interpretability of the complex physiological data but also underscores the potential applicability of the developed model in real-world scenarios for the detection of OSAS. The robustness of the proposed approach, as evidenced by its high accuracy rates, positions it as a promising tool for advancing diagnostic capabilities in the realm of sleep medicine. Future research endeavors may further refine and validate this methodology, paving the way for its integration into clinical practice and contributing to the broader landscape of sleep disorder diagnostics.

Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis.

ObjectivesTo assess the diagnostic utility of smartphone-based measurement in detecting moderate to severe obstructive sleep apnea syndrome (OSAS).MethodsSix databases were thoroughly reviewed. Random-effect models were used to estimate the summary sensitivity, specificity, negative predictive value, positive predictive value, diagnostic odds ratio, summary receiver operating characteristic curve and measured the areas under the curve. To assess the accuracy and precision, pooled mean difference and standard deviation of apnea hypopnea index (AHI) between smartphone and polysomnography (95% limits of agreement) across studies were calculated using the random-effects model. Study methodological quality was evaluated using the QUADAS-2 tool.ResultsEleven studies were analyzed. The smartphone diagnostic odds ratio for moderate-to-severe OSAS (apnea/hypopnea index > 15) was 57.3873 (95% confidence interval [CI]: [34.7462; 94.7815]). The area under the summary receiver operating characteristic curve was 0.917. The sensitivity, specificity, negative predictive value, and positive predictive value were 0.9064 [0.8789; 0.9282], 0.8801 [0.8227; 0.9207], 0.9049 [0.8556; 0.9386], and 0.8844 [0.8234; 0.9263], respectively. We performed subgroup analysis based on the various OSAS detection methods (motion, sound, oximetry, and combinations thereof). Although the diagnostic odds ratios, specificities, and negative predictive values varied significantly (all p < 0.05), all methods afforded good sensitivity (> 80%). The sensitivities and positive predictive values were similar for the various methods (both p > 0.05). The mean difference with standard deviation in the AHI between smartphone and polysomnography was -0.6845 ± 1.611 events/h [-3.8426; 2.4735].ConclusionsSmartphone could be used to screen the moderate-to-severe OSAS. The mean difference between smartphones and polysomnography AHI measurements was small, though limits of agreement was wide. Therefore, clinicians should be cautious when making clinical decisions based on these devices.

Explainable Computer-Aided Detection of Obstructive Sleep Apnea and Depression

Obstructive Sleep Apnea Syndrome (OSAS) and Major Depressive Disorder (MDD) are common conditions associated with poor quality of life. In this work, we aim to classify OSAS and depression in patients with OSAS using machine learning techniques. We have extracted features from electrocardiograms (ECG), electroencephalograms (EEG), and breathing signals from polysomnography (PSG) at specific 5-minute intervals, where the participants’ statuses are known, meaning we do not need breathing signals. These statuses include sleep stage, whether or not they have depression, or an apneic event has occurred. The PSGs were recorded from a total of 118 subjects with a 75/25 split for training and testing and the resultant features were used in sleep staging and classifying OSAS and depression in OSAS patients. Sleep staging was best done with random forest without feature selection, yielding an accuracy of 70.52 % and F1-Score of 69.99 %. The best classification performance of OSAS happened during deep sleep without feature selection and SVM, which yielded an accuracy of 98.36 % and F1-Score of 98.82 %. All sleep stages with Chi2 ANN yielded an accuracy of 72.95 % and F1-Score of 73.43 % for classification of depression in OSAS patients. Results show promise in detecting OSAS and depression in OSAS patients, and the Bland-Altman plot shows that posterior probability provides comparable means of detecting OSAS to the apnea-hypopnea index (AHI). Besides detection of OSAS in depressed patients, this work serves to classify depression and give insights into relevant sleep stages to both of those conditions, allowing better planning for polysomnography.

Obstructive apnea syndrome as a risk factor for the development of postoperative complications in obese patients

Introduction. Early detection of patients with obstructive sleep apnea syndrome (OSAS) using preoperative screening is necessary to reduce risks in the perioperative period.The objective was to assess the effect of OSAS on the perioperative management of patients.Methods and materials. 54 patients with a BMI of more than 30 kg/m2 were examined, for whom a bariatric operation was performed as planned. Before the operation, all patients underwent respiratory polygraph with the calculation of the apnea/ hypopnea index (AHI), mean saturation (SpO2 m) during sleep and spirometry.Results. Patients were divided into 2 groups according to AHI: gr. 1 (n=33) with AHI&lt;15/hour, OSAS, gr. 2 (n=21) with a AHI ≥15/hour. In gr. 2 VC and FVC should be significantly lower than in gr. 1 (p&lt;0.01). A significant decrease in SpO2 m was noted (p&lt;0.001). On the first day after the operation, there was an increase in pCO2 of arterial blood in gr. 2, in comparison with gr. 1 (p&lt;0.05).Conclusions. OSAS can be considered as an independent adverse risk factor for potential perioperative complications. Early detection of OSAS will reduce the risks of postoperative complications.

AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning

Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related breathing disorder. It is caused by an increased upper airway resistance during sleep, which determines episodes of partial or complete interruption of airflow. The detection and treatment of OSAS is particularly important in patients who suffered a stroke, because the presence of severe OSAS is associated with higher mortality, worse neurological deficits, worse functional outcome after rehabilitation, and a higher likelihood of uncontrolled hypertension. The gold standard test for diagnosing OSAS is polysomnography (PSG). Unfortunately, performing a PSG in an electrically hostile environment, like a stroke unit, on neurologically impaired patients is a difficult task; moreover, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. Hence, a simple and automated recognition system to identify OSAS cases among acute stroke patients, relying on routinely recorded vital signs, is highly desirable. The vast majority of the work done so far focuses on data recorded in ideal conditions and highly selected patients, and thus it is hardly exploitable in real-life circumstances, where it would be of actual use. In this paper, we propose a novel convolutional deep learning architecture able to effectively reduce the temporal resolution of raw waveform data, like physiological signals, extracting key features that can be used for further processing. We exploit models based on such an architecture to detect OSAS events in stroke unit recordings obtained from the monitoring of unselected patients. Unlike existing approaches, annotations are performed at one-second granularity, allowing physicians to better interpret the model outcome. Results are considered to be satisfactory by the domain experts. Moreover, through tests run on a widely-used public OSAS dataset, we show that the proposed approach outperforms current state-of-the-art solutions.

A wireless, real-time respiratory effort and body position monitoring system for sleep

Abstract The number of people suffering from a sleep disorder is growing considerably, with obstructive sleep apnea syndrome (OSAS) being the most common one. Unfortunately, it is estimated that at least 75% of OSAS cases are currently undiagnosed and without treatment, mainly due to the lack of availability and high costs of the polysomnography (PSG) test required. OSAS is known to increase the risk for many severe health complications such as hypertension and neurodegenerative conditions, to mention a few, making early diagnosis important. The monitoring of respiratory effort (RE) and body position (BP) during sleep is essential for OSAS detection. Compared to existing commercial systems for respiratory effort monitoring using piezoelectric sensors wired to a central processing system, we present an embedded system solution that is wireless, runs standalone for up to 12 h, and implements on board an extended Kalman filter (EKF) for performing data fusion. An inertial measurement unit (3D acceleration, angular rate, and orientation sensors) is used for real-time measurement of both the respiratory effort and the body position. The experimental tests, when we compare the collected data with a reference signal, acquired by a commercial belt, indicate a general average Pearson correlation coefficient of ρ = 0.963 with results ranging from 0.928 to 0.981, improving over previous works. Low-cost technologies, such as the one presented here, can help to reduce the number of undiagnosed cases of OSAS, and sleep disorders in general, and improve the health of those patients.

Guía de práctica clínica para el diagnóstico y tratamiento quirúrgico del síndrome de apnea obstructiva del sueño en pacientes de dos a ocho años de edad

Introduction and objective: The adenotonsillar hypertrophy is the fundamental cause of childhood obstructive sleep apnea syndrome (OSAS), a frequent pathology that can cause a series of important complications. In certain circumstances, it can be diagnosed without the need of complex tests and treated effectively, with adenotonsillectomy being the choice; since, with the current resources, assuming all the recommendations of the main guidelines are impractical, the number of children that would require referral for diagnostic polysomnography (PSG) and adenotonsillectomy would exceed our capacity. Limiting the guidelines to available means would significantly ignore the health repercussions of childhood OSA, making it difficult to recognize children at risk, as well as being able to offer adequate treatment. Our objective is to establish the recommendations based on the best available scientific evidence to increase, in an efficient way, the diagnosis of pediatric OSAS and to establish the adequate surgical recomendation as well as the safety in the perioperative procedure. Sections: Review of the bibliography of national and international guidelines on the management of childhood OSAS and assessment of own experience derived from clinical practice and prospective follow-up studies of operated patients. Summary of the evidence on childhood OSAS. Resolution of disagreements between scientific evidence and current clinical practice with proposals to minimize them. Recommendations for the diagnostic procedure and the surgical procedure. Perioperative management scheme and post-surgical follow-up. Conclusions: The application of the suggested changes regarding the management of children with suspected OSAS will allow: 1) greater prominence in the process of Primary and Secondary Care provided by general pediatricians to consider adenotonsillectomy; 2) the widespread use of specific questionnaires for the detection of OSAS, nocturnal oximetry and video recording during sleep as assessment tools; and 3) greater availability of complex sleep studies (PSG) to meet international standards for certain conditions.

Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach.

Obstructive sleep apnea (OSA) is a common sleep-related respiratory disease that affects people's health, especially in the elderly. In the traditional PSG-based OSA detection, people's sleep may be disturbed, meanwhile the electrode slices are easily to fall off. In this paper, we study a sleep apnea detection method based on non-contact mattress, which can detect OSA accurately without disturbing sleep. Piezoelectric ceramics sensors are used to capture pressure changes in the chest and abdomen of the human body. Then heart rate and respiratory rate are extracted from impulse waveforms and respiratory waveforms that converted by filtering and processing of the pressure signals. Finally, the Heart Rate Variability (HRV) is obtained by processing the obtained heartbeat signals. The features of the heartbeat interval signal and the respiratory signal are extracted over a fixed length of time, wherein a classification model is used to predict whether sleep apnea will occur during this time interval. Model fusion technology is adopted to improve the detection accuracy of sleep apnea. Results show that the proposed algorithm can be used as an effective method to detect OSA.

Prevalence of sleep-related breathing disorders of inpatients with psychiatric disorders

Sleep-related breathing disorders seriously impair well-being and increase the risk for relevant somatic and psychiatric disorders. Moreover, risk factors for sleep-related breathing disorders are highly prevalent in psychiatric patients. The aim of this study was for the first time in Germany to study the prevalence of obstructive sleep apnea syndrome (OSAS) as the most common form of sleep-related breathing disorder in patients with psychiatric disorders. In 10psychiatric hospitals in Germany and 1 hospital in Switzerland, atotal of 249 inpatients underwent an 8‑channel sleep polygraphy to investigate the prevalence of sleep apnea in this group of patients. With aconspicuous screening result of 23.7% of the subjects, a high prevalence of sleep-related breathing disorders was found to occur among this group of patients. Male gender, higher age and high body mass index (BMI) were identified as positive risk factors for the detection of OSAS. The high prevalence indicates that sleep apnea is acommon sleep disorder among psychiatric patients. Although OSAS can lead to substantial disorders of the mental state and when untreated is accompanied by serious somatic health problems, screening procedures are not part of the routine work-up in psychiatric hospitals; therefore, sleep apnea is presumably underdiagnosed in psychiatric patients. In view of the results of this and previous studies, this topic complex should be the subject of further research studies.

Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea

A variety of statistical models based on overnight oximetry has been proposed to simplify the detection of children with suspected obstructive sleep apnea syndrome (OSAS). Despite the usefulness reported, additional thorough comparative analyses are required. This study was aimed at assessing common binary classification models from oximetry for the detection of childhood OSAS. Overnight oximetry recordings from 176 children referred for clinical suspicion of OSAS were acquired during in-lab polysomnography. Several training and test datasets were randomly composed by means of bootstrapping for model optimization and independent validation. For every child, blood oxygen saturation (SpO2) was parameterized by means of 17 features. Fast correlation-based filter (FCBF) was applied to search for the optimum features. The discriminatory power of three statistical pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR). The performance of each automated model was evaluated for the three common diagnostic polysomnographic cutoffs in pediatric OSAS: 1, 3, and 5 events/h. Best screening performances emerged using the 1 event/h cutoff for mild-to-severe childhood OSAS. LR achieved 84.3% accuracy (95% CI 76.8-91.5%) and 0.89 AUC (95% CI 0.83-0.94), while QDA reached 96.5% PPV (95% CI 90.3-100%) and 0.91 AUC (95% CI 0.85-0.96%). Moreover, LR and QDA reached diagnostic accuracies of 82.7% (95% CI 75.0-89.6%) and 82.1% (95% CI 73.8-89.5%) for a cutoff of 5 events/h, respectively. Automated analysis of overnight oximetry may be used to develop reliable as well as accurate screening tools for childhood OSAS.

Flexible surface acoustic wave respiration sensor for monitoring obstructive sleep apnea syndrome

Obstructive sleep apnea syndrome (OSAS) has received much attention in recent years due to its significant harm to human health and high morbidity rate. A respiration monitoring system is needed to detect OSAS, so that the patient can receive treatment in a timely manner. Wired and wireless OSAS monitoring systems have been developed, but they require a wire connection and batteries to operate, and they are bulky, heavy and not user-friendly. In this paper, we propose the use of a flexible surface acoustic wave (SAW) microsensor to detect and monitor OSAS by measuring the humidity change associated with the respiration of a person. SAW sensors on rigid 128° YX LiNbO3 substrate are also characterized for this application. Results show both types of SAW sensors are suitable for OSAS monitoring with good sensitivity, repeatability and reliability, and the response time and recovery time for the flexible SAW sensors are 1.125 and 0.75 s, respectively. Our work demonstrates the potential for an innovative flexible microsensor for the detection and monitoring of OSAS.

Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home

Untreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO2) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.

Pediatric sleep-disordered breathing: Role of the dentist.

In this review article, the role of the dentist in the evaluation and treatment of snoring and Obstructive Sleep Apnea Syndrome (OSAS) in children is described. Snoring and OSAS in children is receiving increased awareness, with reported rates approximating 10% of children who regularly snore, and up to 4% who suffer from OSAS. OSAS in children may have serious developmental and behavioral consequences. Apnea Hypopnea Index (AHI) is the main outcome of the polysomnography test, but its diagnostic values differ from children to adults, as do treatment approaches. A comprehensive literature search of publications from 1973 to 2017 in the PubMed Direct databases was performed to collect information about snoring and OSAS in children. The search was limited to peer-reviewed articles written in English with a few exceptions in other languages. Dentists play a significant role in early detection of OSAS, helping in reducing and preventing its serious consequences. A multidisciplinary treatment team, which manages and treats OSAS, should include the dentist in addition to the sleep specialist and the ENT physician.

Obstructive Sleep Apnea Syndrome-Prevalence and Screening in the Preadmission Clinic

Objective: The aim of this study was to evaluate the suitability of 2 different screening methods for the detection of obstructive sleep apnea syndrome (OSAS) in the preadmission clinic at a university hospital. Methods: Patients were screened for OSAS using 2 different methods. Method 1: If they were deemed conspicuous by answering three specific medical history questions (daytime sleepiness, snoring and BMI (body mass index)), the patients hat to fill in the Epworth Sleepiness Scale (ESS). Method 2: All patients had to fill in the ESS. An ESS-score of 10 or higher was deemed conspicuous and regarded as a high risk for OSAS. The length of hospital stay of these patients was compared to a matched non OSAS group. Results: Of the 4355 (100%) patients evaluated with method 1, 109 (2.5%) had a known OSAS and 631 (14.5%) had to fill in the ESS-Score in consequence of the 3 specific medical history questions. 82 (13% of the 631 patients) of them reached a score ≥ 10, which equals 1.9% of all patients screened with method 1. At least 191 (4.4%) of all patients who were screened with method 1, were declared as conspicuous. Of the 3415 (100%) patients detected with method 2, 115 patients (3.35%) had a known OSAS. 115 patients (3.35%) achieved an ESS score ≥ 10. Thus 230 patients (6.7%) screened with method 2 were classified as conspicuous. The deviation of mean hospital stay was significantly higher in the group of conspicuous patients compared to the inconspicuous patients. Conclusion: At the pre-admission clinic more patients with OSAS or high risk for OSAS were detected using a standardized screening tool (ESS-Questionnaire) compared to specific questions alone. Due to the increased risk for perioperative complications, the preoperative detection is essential for perioperative management and to initiate further diagnostics and therapy.

Changing indications and socio-demographic determinants of (adeno)tonsillectomy among children in England--are they linked? A retrospective analysis of hospital data.

ObjectiveTo assess whether increased awareness and diagnosis of obstructive sleep apnoea syndrome (OSAS) and national guidance on tonsillectomy for recurrent tonsillitis have influenced the socio-demographic profile of children who underwent tonsillectomy over the last decade.MethodRetrospective time-trends study of Hospital Episodes Statistics data. We examined the age, sex and deprivation level, alongside OSAS diagnoses, among children aged <16 years who underwent (adeno)tonsillectomy in England between 2001/2 and 2011/12.ResultsAmong children aged <16 years, there were 29,697 and 27,732 (adeno)tonsillectomies performed in 2001/2 and 2011/12, respectively. The median age at (adeno)tonsillectomy decreased from 7 (IQR: 5–11) to 5 (IQR: 4–9) years over the decade. (Adeno)tonsillectomy rates among children aged 4–15 years decreased by 14% from 350 (95%CI: 346–354) in 2001/2 to 300 (95%CI: 296–303) per 100,000 children in 2011/12. However, (adeno)tonsillectomy rates among children aged <4 years increased by 58% from 135 (95%CI: 131–140) to 213 (95%CI 208–219) per 100,000 children in 2001/2 and 2011/2, respectively. OSAS diagnoses among children aged <4 years who underwent surgery increased from 18% to 39% between these study years and the proportion of children aged <4 years with OSAS from the most deprived areas increased from 5% to 12%, respectively.Conclusions(Adeno)tonsillectomy rates declined among children aged 4–15 years, which reflects national guidelines recommending the restriction of the operation to children with more severe recurrent throat infections. However, (adeno)tonsillectomy rates among pre-school children substantially increased over the past decade and one in five children undergoing the operation was aged <4 years in 2011/12.The increase in surgery rates in younger children is likely to have been driven by increased awareness and detection of OSAS, particularly among children from the most deprived areas.

A tether-free Obstructive Sleep Apnea Syndrome (OSAS) detection mattress

W-K. WANG, C-H. CHEN, W-C. HUANG, R-H. SU, Y-W. LIU. A tether-free Obstructive Sleep Apnea Syndrome (OSAS) detection mattress. Gerontechnology 2014;13(2):301; doi:10.4017/ gt.2014.13.02.390.00 Purpose Obstructive Sleep Apnea Syndrome (OSAS) may lead to daytime lethargy. More severe OSAS may decrease memory and attention, and may even cause death due to hypoxia. Currently, obstructive sleep apnea affects 4-5% of the general population, and often occurs in obese and elderly adults1,2. To our knowledge, the majority of products currently on the market can only detect snoring by microphone sensor. However, similar methods are ineffective for OSAS, as the condition is soundless. OSAS monitoring devices typically monitor chest movement as a result of breathing for signs of faintness; however, these monitors are uncomfortable and may affect sleep quality. This paper presents the development of an OSAS detection mattress, WhizMattress, which allows for the detection of OSAS events without wearable sensors. Method Ultra-Wide Band (UWB) technology can detect slight movement without contact. The detection process also does not affect human health. Note that the electromagnetic wave of UWB is lower than the 1 ppm of typical cell phones. Furthermore, UWB can pass through liquid and solid states. Therefore, we inserted a UWB sensor into a memory foam mattress (IMAGER-37). The detection area is between the user's chest and abdomen. If movement in this area becomes faint, an OSAS event is recognized. Figure 1 shows the pattern of breathing in the Labview system. From Figure 1, breathing becomes smoother when OSAS occurs. In order to determine the accuracy of WhizMattress, a comparative clinical test between WhizMattress and Polysomnography (PSG) was implemented. A healthy adult (40-year-old male, overweight with a BMI of 26.4) was recruited for sleep data collection. The subject wore a PSG sensor and lay on the WhizMattress for 423.1 min (Figure 2). Thirty six OSAS events were detected by both devices, and 37 by PSG alone. Therefore, the sensitivity of the WhizMattress (i.e., the probability of WhizMattress and PSG judge OSAS event under PSG judge OSAS event) is 36/74 = 49.3%. Note that the subject sometimes laid on the non-detection area. In these cases, the breathing was undetected. Results & Discussion WhizMattress not only can detect OSAS without wearable sensors, but also can provide a sleep quality evaluation in home healthcare. In future work, more comparative experiments will be performed for verifying the mattress' accuracy.

SAT0110 Evaluation of Sleep Quality and the Effects of anti Tnf-Alpha Treatment on Sleep in Rheumatoid Arthritis Patients

Backgroundİmpaired sleep quality negatively influences quality of life, morbidity/mortality, and pain perception. Tumor necrosis factor-alpha (TNF-α), which plays a role in the pathogenesis of rheumatoid arthritis (RA), is also a...

Reliability of SleepStrip as a screening test in obstructive sleep apnea patients.

Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder and related to multiple conditions that cause mortality in adults. In the present study, reliability of SleepStrip, a disposable screening device for detection of OSAS, is tested. In this prospective, nonrandomized double-blinded single cohort study at an academic health center, the performance of the SleepStrip in detecting respiratory events and establishing an SleepStrip score (Sscore) in domestic use were compared to the apnea-hypopnea index (AHI) obtained by the standard polysomnography (PSG) recordings in the sleep laboratory. Forty-one patients who have the PSG results participated the study and wore the SleepStrips at home. Test efficiency rate was 75% and there was a positive correlation between PSG-AHI scores and Sscores (r = 0.71, p < 0.001). However, diagnostic accuracy analysis showed that the correlation between Sscores and PSG-AHI scores were significant only at AHI > 30 levels. The SleepStrip has 100% specificity and positive predictive values, but it also has low negative predictive and sensitivity values. The SleepStrip is not a reliable screening test in differential diagnosis among simple snorers, mild, moderate and severe OSAS patients. However, high Sscores highly indicate the presence of moderate-severe OSAS. We can safely send these patients to split-night PSG and continuous, automatic, bi-level positive airway pressure (CPAP/BPAP/APAP) titration at the same night. The SleepStrip may increase the effective use of the sleep laboratories.

Detection of obstructive sleep apnoea by an electronic nose

Diagnosis of obstructive sleep apnoea syndrome (OSAS) is technically demanding, cost-intensive and time-consuming. The measurement of volatile organic compounds by an electronic nose is an innovative method that determines distinct molecular patterns of exhaled breath in different patient groups. We addressed the following questions: What is the diagnostic accuracy of an electronic nose in the detection of OSAS and the ability to detect effects of standard therapy in patients with OSAS? Are these results related to changes in distinct markers of airway inflammation and extracellular remodelling? We included 40 OSAS patients and 20 healthy controls. Exhaled breath of all participants was analysed using the Cyranose 320 electronic nose. Pharyngeal washings were performed to sample the upper airway compartment. For statistical analysis linear discriminant analysis was employed. We identified a linear discriminant function separating OSAS from control (p<0.0001). The corresponding area under the receiver-operating curve was 0.85 (95% CI 0.75-0.96; sensitivity 0.93 and specificity 0.7). In pharyngeal washing fluids of OSAS patients, we observed higher levels of α1-antitrypsin and markers of extracellular remodelling compared to controls. The electronic nose can distinguish between OSAS patients and controls with high accuracy.