Severity Of Sleep Apnea-hypopnea Syndrome Research Articles

Leveraging Attention-reinforced UWB Signals to Monitor Respiration during Sleep

The respiration state during overnight sleep is an important indicator of human health. However, existing contactless solutions for sleep respiration monitoring either perform in controlled environments and have low usability in practical scenarios or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events in patients. In this article, we propose Respnea, a non-intrusive sleep respiration monitoring system using an ultra-wideband device. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different subject states. Further, we construct a deep learning model that adopts a multi-head self-attention mechanism and learns the patterns implicit in the respiration signals to distinguish sleep respiration events at a granularity of seconds. To improve the generalization of the model, we propose a contrastive learning strategy to learn a robust representation of the respiration signals. We deploy our system in hospital and home scenarios and conduct experiments on data from healthy subjects and patients with sleep disorders. The experimental results show that Respnea achieves high temporal coverage and low errors (a median error of 0.27 bpm) in respiration rate estimation and reaches an accuracy of 94.44% on diagnosing the severity of sleep apnea-hypopnea syndrome.

The impact of Obstructive Sleep Apnea Hypopnea syndrome severity on depression and anxiety disorders

IntroductionObstructive sleep apnea hypopnea syndrome (OSAHS) is a chronic source of stress that can alter the emotional state of affected patients.ObjectivesThis study aimed to assess the impact of OSAHS severity on depression and anxiety disorders in a Tunisian population of apneic patients.MethodsWe conducted a cross-sectional study, involving 40 patients diagnosed with OSAHS by polysomnography in the Sleep unit, department of Neurophysilogy at Sahloul university hospital in Sousse, Tunisia. Anxiety and depressive disorders were detected using the Arabic version of the HADS (Hospital Anxiety and Depression Scale).Results The mean age was 49.7 ± 7.87 years with a sex ratio of 1.1. The mean apnea-hypopnea index (AHI) was 29.72. OSAHS was mild, moderate and severe in 40%, 22.5% and 37.5% of cases respectively. One third (30%) of patients received a treatment with continuous positive airway pressure (CPAP). The prevalence of depression in the study’s patients, according to the HADS, was 56.4% and that of anxiety was 59%. There was a positive linear relationship between AHI and scores of depression and anxiety (p=0.045 and p=0.037 respectively). Similarly, a significant association was found between HAD scores and treatment with CPAP (p<0.05).Conclusions These results show a high frequency of anxiety-depressive disorders in patients with OSAHS. Severity of OSAHS and CPAP treatment proved to be determining factors in anxiety and depressive disorders, hence the importance of detecting these disorders in order to improve patients’ quality of life.Disclosure of InterestNone Declared

Association between adherence to the mediterranean diet, physical activity, and sleep apnea-hypopnea syndrome (SAHS) in a middle-aged population with cardiovascular risk: Insights from the ILERVAS cohort

IntroductionSleep Apnea-Hypopnea Syndrome (SAHS) is a common sleep disorder influenced by factors like age, gender, and obesity. The Mediterranean Diet (MedDiet) and physical activity have shown health benefits in lung diseases, but their effects on SAHS remain underexplored. MethodsIn a cross-sectional analysis of 678 middle-aged individuals with low-to-moderate cardiovascular risk from the ILERVAS cohort, we assessed adherence to the MedDiet and physical activity levels using validated tools. Sleep parameters, SAHS severity, and excessive daytime sleepiness were evaluated through non-attended cardiorespiratory polygraphy and the Epworth Sleepiness Scale. Multinomial logistic regression models were employed to assess the relationship between MedDiet adherence, physical activity, and SAHS severity. ResultsThe prevalence of severe, moderate, and mild SAHS was 15.5%, 23.2% and 36.1%, respectively. We found no significant associations between adherence to the MedDiet, physical activity levels, and the presence or severity of SAHS. However, we noted a significant interaction between MedDiet and physical activity with minimum SpO2 values (p = 0.049). Notably, consuming more than one serving of red meat per day was independently associated with a higher risk of moderate SAHS [OR = 2.65 (1.29–5.44), p = 0.008]. ConclusionIndividually, MedDiet adherence and physical activity did not show independent correlations with SAHS. However, when considered together, a minimal but significant effect on minimum SpO2 was observed. Additionally, red meat consumption was associated with a moderate risk of SAHS. Further research is necessary to comprehend the intricate connections between lifestyle factors and sleep-breathing disorders, with a focus on personalized approaches for high-risk populations.

Dental caries status in adults with sleep apnea - hypopnea syndrome.

BackgroundA study is made of dental caries in a group of adults with sleep apnea-hypopnea syndrome (SAHS), establishing comparisons with healthy individuals corresponding to the same population. Material and MethodsA case-control series was analyzed, including patients with recently diagnosed SAHS and individuals without SAHS. Dental examinations were made to record the DMF (decayed, missing, filled) dental score, and demographic, lifestyle and clinical data were collected. ResultsA total of 114 participants (60 SAHS cases and 54 controls) were included in the study. Although the mean DMF score in the SAHS group was higher than in the control group (7.03 versus 4.81, respectively), the multivariate regression analysis did not find the difference to be statistically significant (p=0.351). However, a significant correlation was observed between the DMF score and age (r=0.41; p<0.001) and the apnea-hypopnea index (AHI)(r=0.31; p=0.003). ConclusionsOlder age and greater severity of SAHS are associated to higher DMF scores. However, the diagnosis of SAHS alone does not influence dental caries status. Key words:Dental caries, sleep apnea syndrome, oral health, DMF index.

Interactions of central and autonomic nervous systems in patients with sleep apnea-hypopnea syndrome during sleep.

Sleep apnea-hypopnea syndrome (SAHS) is an independent risk factor for various cardiovascular and cerebrovascular diseases, but the underlying relationship of its physiological subsystems remains unclear. Thus, we aimed to investigate the effect of SAHS on central and autonomic nervous system (CNS-ANS) interactions during sleep. Thirty-five patients with SAHS and 19 healthy age-matched controls underwent overnight polysomnography. The absolute spectral powers of five frequency bands from six EEG channels and ECG morphological features (HR, PRinterval, QTinterval) were calculated. Multivariable transfer entropy was applied to analyze the differences of the CNS-ANS network interactions between patients withSAHS of different severities and healthy controls during deep, light, and rapid eye movement sleep. The CNS-ANS network interacted bidirectionally in all researched groups, with the cardiac information modulating the brain activity. The information strength from QT to most EEG components and PR to some EEG components was significantly affected by SAHS severity during light sleep, which indicates the coupling features of QT-brain nodes are important indicators. The driver effects from the β-band significantly increased in patients withSAHS. Respiratory events may be the main reason for the CNS-ANS interaction changes in SAHS. These findings help explain the physiological regulation process of SAHS and provide valuable information for analysis of the development of SAHS-related cardiovascular and chronic diseases.

A novel approach to diagnose sleep apnea using enhanced frequency extraction network

Sleep apnea-hypopnea syndrome (SAHS), as a widespread respiratory sleep disorder, if left untreated, can lead to a series of pathological changes. By using Polysomnography (PSG), traditional SAHS diagnosis tends to be complex and costly. Nasal airflow (NA) is the most direct reflection of the severity of SAHS. Therefore, we try to take advantage of NA signals that can be easily recorded by wearable devices. In this paper, we present an automatic detection approach of SAH events based on single-channel signal. Through this approach, an enhanced frequency extraction network is designed, which factorizes the mixed feature maps by their frequencies. And the spatial resolution of low-frequency components is reduced so as to save spending. Besides, in our research, the vanilla convolution block of the high-frequency components are replaced by residual blocks and smaller groups of filters with bigger size kernels. And we use the spatial attention module to facilitate feature extraction. Compared with state-of-the-art networks in this field, the promising results reveal that the proposed network for SAH events multiclass classification shows outstanding performance with accuracy of 91.23%, sensitivity of 90.81% and specificity of 90.59%. Thus, we believe that our approach, as a low-cost and high-efficiency solution, shows a great potential for detecting SAH events.

Estimating a Sleep Apnea Hypopnea Index Based on the ERB Correlation Dimension of Snore Sounds.

This paper proposes a new perspective of analyzing non-linear acoustic characteristics of the snore sounds. According to the ERB (Equivalent Rectangular Bandwidth) scale used in psychoacoustics, the ERB correlation dimension (ECD) of the snore sound was computed to feature different severity levels of sleep apnea hypopnea syndrome (SAHS). For the training group of 93 subjects, snore episodes were manually segmented and the ECD parameters of the snores were extracted, which established the gaussian mixture models (GMM). The nocturnal snore sound of the testing group of another 120 subjects was tested to detect SAHS snores, thus estimating the apnea hypopnea index (AHI), which is called AHIECD. Compared to the AHIPSG value of the gold standard polysomnography (PSG) diagnosis, the estimated AHIECD achieved an accuracy of 87.5% in diagnosis the SAHS severity levels. The results suggest that the ECD vectors can be effective parameters for screening SAHS.

Usefulness of Home Overnight Pulse Oximetry in Patients with Suspected Sleep-Disordered Breathing.

Methods Prospective study conducted in a university hospital. Subjects with a clinical suspicion of SAHS were included. All of them underwent home polygraphy and oximetry on the same night. A correlation was made between the apnea-hypopnea index (AHI) and the oximetry variables. The variable with the highest diagnostic value was calculated using the area under the curve (AUC), and the best cut-off point for discriminating between patients with SAHS and severe SAHS was identified. Results One hundred and four subjects were included; 73 were men (70%); mean age was 52 ± 10.1 years; body mass index was 30 ± 4.1, and AHI = 29 ± 23.2/h. A correlation was observed between the AHI and oximetry variables, particularly ODI3 (r = 0.850; P < 0.001) and ODI4 (r = 0.912; P < 0.001). For an AHI ≥ 10/h, the ODI3 had an AUC = 0.941 (95% confidence interval (CI) = 0.899–0.982) and the ODI4, an AUC = 0.984 (95% CI = 0.964–1), with the ODI4 having the best cut-off point (5.4/h). Similarly, for an AHI ≥ 30/h, the ODI4 had an AUC = 0.922 (95% CI = 0.859–0.986), with the best cut-off point being 10.5/h. Conclusion Nocturnal oximetry is useful for diagnosing and evaluating the severity of SAHS. The ODI4 variable was most closely correlated with AHI for both diagnosis.

Automatic Assessment of Pediatric Sleep Apnea Severity Using Overnight Oximetry and Convolutional Neural Networks.

In this study, we use the overnight blood oxygen saturation (SpO2) signal along with convolutional neural networks (CNN) for the automatic estimation of pediatric sleep apnea-hypopnea syndrome (SAHS) severity. The few preceding studies have focused on the application of conventional feature extraction methods to obtain information from the SpO2 signal, which may omit relevant data related to the illness. In contrast, deep learning techniques are able to automatically learn features from raw input signal. Thus, we propose to assess whether CNN, a deep learning algorithm, could automatically estimate the apnea-hypopnea index (AHÍ) from nocturnal oximetry to help establish pediatric SAHS presence and severity. A database of 746 SpO2 recordings is involved in the study. CNN was trained using 20-min segments from the SpO2 signal in the training set (400 subjects). Hyperparameters of the CNN architecture were tuned using a validation set (100 subjects). This model was applied to a test set (246 subjects), in which the final AHI of each patient was obtained as the average of the output of the CNN for all the segments of the corresponding SpO2 signal. The AHI estimated by the CNN showed a promising diagnostic performance, with 74.8%, 90.7%, and 95.1% accuracies for the common AHI severity thresholds of 1, 5, and 10 events per hour (e/h), respectively. Furthermore, this model reached 28.6, 32.9, and 120.0 positive likelihood ratios for the above-mentioned AHI thresholds. This suggests that the information extracted from the oximetry signal by deep learning techniques may be useful to both establish pediatric SAHS and its severity.

Sympathetic Hyperactivity and Sleep Disorders in Individuals With Type 2 Diabetes.

Introduction: Many studies on the impact of type 2 diabetes mellitus (T2DM) on sleep breathing have shown a higher prevalence and severity of sleep apnea-hypopnea syndrome (SAHS) in those with T2DM. Moreover, an increased activity of the sympathetic nervous system has been described in both pathologies. This cross-sectional study aimed to assess sympathetic activity in patients with T2DM, and to investigate the relationship between sympathetic activity and polysomnographic parameters.Materials and Methods: Thirty-six patients with T2DM without known clinical macrovascular nor pulmonary disease and 11 controls underwent respiratory polygraphy, and their cardiac variability and 24-h urine total metanephrines were measured.Results: SAHS was highly prevalent with a mean apnea-hypopnea index (AHI) in the range of moderate SAHS. In patients with T2DM, the nocturnal concentration of total metanephrines in urine were higher than diurnal levels [247.0 (120.0–1375.0) vs. 210.0 (92.0–670.0), p = 0.039]. The nocturnal total metanephrine concentration was positively and significantly associatedwith the percentage of sleeping time spent with oxygen saturation <90%(CT90). In the entire population and in subjects with T2DM, the multivariate regression analysis showed a direct interaction between the nocturnal concentration of urine metanephrines and the CT90.Conclusion: These findings suggest that the increase in sympathetic activity previously described in patients with T2DM could be mediated through nocturnal breathing disturbances. The diagnosis and treatment of SAHS may influence sympathetic activity disorders and may contribute to an improvement in T2DM and cardiovascular risk.

Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis

Background and objectiveIn-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). Methods946 AF and blood oxygen saturation (SpO2) recordings from children ages 0–13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea–hypopnoea index (AHI). Results8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. ConclusionsRP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.

Detrended fluctuation analysis of the oximetry signal to assist in paediatric sleep apnoea–hypopnoea syndrome diagnosis

Objective: To evaluate whether detrended fluctuation analysis (DFA) provides information that improves the diagnostic ability of the oximetry signal in the diagnosis of paediatric sleep apnoea–hypopnoea syndrome (SAHS). Approach: A database composed of 981 blood oxygen saturation (SpO2) recordings in children was used to extract DFA-derived features in order to quantify the scaling behaviour and the fluctuations of the SpO2 signal. The 3% oxygen desaturation index (ODI3) was also computed for each subject. Fast correlation-based filter (FCBF) was then applied to select an optimum subset of relevant and non-redundant features. This subset fed a multi-layer perceptron (MLP) neural network to estimate the apnoea–hypopnoea index (AHI). Main results: ODI3 and four features from the DFA reached significant differences associated with the severity of SAHS. An optimum subset composed of the slope in the first scaling region of the DFA profile and the ODI3 was selected using FCBF applied to the training set (60% of samples). The MLP model trained with this feature subset showed good agreement with the actual AHI, reaching an intra-class correlation coefficient of 0.891 in the test set (40% of samples). Furthermore, the estimated AHI showed high diagnostic ability, reaching an accuracy of 82.7%, 81.9%, and 91.1% using three common AHI cut-offs of 1, 5, and 10 events per hour (e h−1), respectively. These results outperformed the overall performance of ODI3. Significance: DFA may serve as a reliable tool to improve the diagnostic performance of oximetry recordings in the evaluation of paediatric patients with symptoms suggestive of SAHS.

Improving the Diagnostic Ability of Oximetry Recordings in Pediatric Sleep Apnea-Hypopnea Syndrome by Means of Multi-Class AdaBoost.

Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may impose many negative effects on the health and development of children. Due to the drawbacks of overnight polysomnography (PSG), the gold standard diagnosis technique, automated analysis of nocturnal oximetry has emerged as a simplified alternative. In order to improve diagnosis ability of oximetry, we propose to evaluate the usefulness of AdaBoost, a classification boosting algorithm, in the context of pediatric SAHS. A database composed of 981 SpO2 recordings from pediatric subjects was used. For this purpose, a signal processing approach divided into two main stages was conducted: (i) feature extraction, where 3% oxygen desaturation index (ODI3), spectral, and nonlinear features were computed from the oximetry signal, and (ii) AdaBoost classification, where an AdaBoost.M2 model was trained with these features in order to determine the severity of pediatric SAHS according to the apnea-hypopnea index (AHI): AHI<1 events per hour (e/h), 1≤AHI<5 e/h, and AHI≥5 e/h. Our AdaBoost.M2 model achieved a Cohen's kappa of 0.474 in an independent test set in the 3-class classification task. In addition, high accuracies were obtained when using the AHI cutoffs for diagnosis of mild (AHI=1 e/h) and moderate-to-severe (AHI=5 e/h) SAHS: 80.9% and 82.9%, respectively. These results achieved slightly higher diagnostic accuracies than ODI3 as well as state-of-the-art studies. Therefore, AdaBoost could help to enhance the diagnostic ability of the oximetry signal to assess pediatric SAHS severity.

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings.

Complexity, costs, and waiting list issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients' homes and were used to automatically obtain statistical, spectral, nonlinear, and clinical SAHS-related information. Relevant, nonredundant data from these analyses were subsequently used to train and validate four machine-learning methods with the ability to classify SpO2 signals into one of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multilayer perceptron, and AdaBoost) outperformed the diagnostic ability of the conventionally used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen's κ in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine-learning can be used along with SpO2 information acquired at a patients' home to help in SAHS diagnosis simplification.

The Development of a Dual-Radar System with Automatic Hypopnea Threshold Optimization for Contact-Free Sleep Apnea-Hypopnea Syndrome Screening

Full-night polysomnography (PSG) examination is regarded as the gold standard for the diagnosis of sleep apnea-hypopnea syndrome (SAHS). However, PSG requires the placement of multiple sensors on the head, face, and chest, which can impose a heavy strain on patients. Therefore, in the present study, we aimed to develop a contact-free, stand-alone SAHS screening system that eliminates body movement artifacts based on automatic optimization of the hypopnea threshold. Doppler radar sensors were placed beneath a mattress. In order to achieve high sensitivity and specificity, the hypopnea was based on the average amplitude of respiration during the full sleep period. The threshold was determined via receiver operating characteristic (ROC) analysis using PSG as a reference. We conducted full-night clinical tests of the proposed system in 27 patients with suspected SAHS (49 ± 12 years) at Tomei Atsugi Hospital. When predicting the severity of SAHS with an apnea-hypopnea index (AHI) of &gt;30/h using PSG as a reference, the proposed system achieved a sensitivity of 100% and a specificity of 100%. These results represent a drastic improvement over those of our previous study (sensitivity: 90%; specificity: 79%).

Association between Sleep Apnea Hypopnea Syndrome and the Risk of Atrial Fibrillation: A Meta-Analysis of Cohort Study.

Numerous reports have been done to seek the relationship between sleep apnea hypopnea syndrome (SAHS) and the risk of atrial fibrillation (AF). However, definite conclusion has not yet been fully established. We examined whether SAHS increases AF incidence in common population and summarized all existing studies in a meta-analysis. We summarized the current studies by searching related database for potential papers of the association between SAHS and the risk of AF. Studies that reported original data or relative risks (RRs) with 95% confidence intervals (CIs) for the associations were included. Sensitivity analyses were performed by omitting each study iteratively and publication bias was detected by Begg's tests. Eight eligible studies met the inclusion criteria. Fixed effects meta-analysis showed that SAHS increased AF risk in the common population (RR = 1.70, 95% CI: 1.53–1.89, P = 0.002, I2 = 69.2%). There was a significant association between mild SAHS and the risk of AF (RR = 1.52, 95% CI: 1.28–1.79, P = 0.01, I2 = 78.4%), moderate SAHS (RR = 1.88, 95% CI: 1.55–2.27, P = 0.017, I2 = 75.6%), and severe SAHS (RR = 2.16, 95% CI: 1.78–2.62, P < 0.001, I2 = 91.0%). The results suggest that sleep apnea hypopnea syndrome could increase the risk of AF, and the higher the severity of SAHS, the higher risk of atrial fibrillation.

Utility of bispectrum in the screening of pediatric sleep apnea-hypopnea syndrome using oximetry recordings

Background and objectiveThe aim of this study was to assess the utility of bispectrum-based oximetry approaches as a complementary tool to traditional techniques in the screening of pediatric sleep apnea-hypopnea syndrome (SAHS). Methods298 blood oxygen saturation (SpO2) signals from children ranging 0–13 years of age were recorded during overnight polysomnography (PSG). These recordings were divided into three severity groups according to the PSG-derived apnea hypopnea index (AHI): AHI < 5 events per hour (e/h), 5 ≤ AHI < 10 e/h, AHI ≥ 10 e/h. For each pediatric subject, anthropometric variables, 3% oxygen desaturation index (ODI3) and spectral features from power spectral density (PSD) and bispectrum were obtained. Then, the fast correlation-based filter (FCBF) was applied to select a subset of relevant features that may be complementary, excluding those that are redundant. The selected features fed a multiclass multi-layer perceptron (MLP) neural network to build a model to estimate the SAHS severity degrees. ResultsAn optimum subset with features from all the proposed methodological approaches was obtained: variables from bispectrum, as well as PSD, ODI3, Age, and Sex. In the 3-class classification task, the MLP model trained with these features achieved an accuracy of 76.0% and a Cohen's kappa of 0.56 in an independent test set. Additionally, high accuracies were reached using the AHI cutoffs for diagnosis of moderate (AHI = 5 e/h) and severe (AHI = 10 e/h) SAHS: 81.3% and 85.3%, respectively. These results outperformed the diagnostic ability of a MLP model built without using bispectral features. ConclusionsOur results suggest that bispectrum provides additional information to anthropometric variables, ODI3 and PSD regarding characterization of changes in the SpO2 signal caused by respiratory events. Thus, oximetry bispectrum can be a useful tool to provide complementary information for screening of moderate-to-severe pediatric SAHS.

A Bayesian neural network approach to compare the spectral information from nasal pressure and thermistor airflow in the automatic sleep apnea severity estimation.

In the sleep apnea-hypopnea syndrome (SAHS) context, airflow signal plays a key role for the simplification of the diagnostic process. It is measured during the standard diagnostic test by the acquisition of two simultaneous sensors: a nasal prong pressure (NPP) and a thermistor (TH). The current study focuses on the comparison of their spectral content to help in the automatic SAHS-severity estimation. The spectral analysis of 315 NPP and corresponding TH recordings is firstly proposed to characterize the conventional band of interest for SAHS (0.025-0.050 Hz.). A magnitude squared coherence analysis is also conducted to quantify possible differences in the frequency components of airflow from both sensors. Then, a feature selection stage is implemented to assess the relevance and redundancy of the information extracted from the spectrum of NPP and TH airflow. Finally, a multiclass Bayesian multi-layer perceptron (BY-MLP) was used to perform an automatic estimation of SAHS severity (no-SAHS, mild, moderate, and severe), by the use of the selected spectral features from: airflow NPP alone, airflow TH alone, and both sensors jointly. The highest diagnostic performance was reached by BY-MLP only trained with NPP spectral features, reaching Cohen's κ = 0.498 in the overall four-class classification task. It also achieved 91.3%, 84.9%, and 83.3% of accuracy in the binary evaluation of the 3 apnea-hypopnea index cut-offs (5, 15, and 30 events/hour) that define the four SAHS degrees. Our results suggest that TH sensor might be not necessary for SAHS severity estimation if an automatic comprehensive characterization approach is adopted to simplify the diagnostic process.

Non-contact diagnostic system for sleep apnea-hypopnea syndrome based on amplitude and phase analysis of thoracic and abdominal Doppler radars.

Full-night polysomnography (PSG) has been recognized as the gold standard test for sleep apnea-hypopnea syndrome (SAHS). However, PSG examinees are physically restrained for the full night by many contact sensors and obtrusive connecting cables, inducing mental stress. We developed a non-contact SAHS diagnostic system that can detect apneic events without inducing stress in monitored individuals. Two Doppler radars were installed beneath the mattress to measure the vibrations of the chest and abdomen, respectively. Our system determines apnea and hypopnea events when the radar output amplitude decreases by <20 and 70 %, respectively, of the amplitude of a normal breath (without SAHS events). Additionally, we proposed a technique that detects paradoxical movements by focusing on phase differences between thoracic and abdominal movements, and were able to identify three types of sleep apnea: obstructive, central, and mixed. Respiratory disturbance indexes obtained showed a higher correlation (r = 94 %) with PSG than with pulse oximetry (r = 89 %). When predicting the severity of SAHS with an apnea-hypopnea index (AHI) of >15/h or >30/h using PSG as a reference, the radar system achieved a sensitivity of 96 and 90 %, and a specificity of 100 and 79 % with an AHI of >15/h and >30/h, respectively. The proposed radar system can be used as an alternative to the current airflow sensor, and to chest and abdomen belts for apnea-hypopnea evaluation.

The impacts of obstructive sleep apnea hypopnea syndrome severity and surgery intervention on psychological and behavioral abnormalities and postoperative recovery in pediatric patients.

BackgroundThe aim of this study was to investigate the degree of impact of obstructive sleep apnea hypopnea syndrome (OSAHS) severity on pediatric psychological and behavioral abnormalities.Material/MethodsFifty-one children aged 5–12 years with a confirmed diagnosis of OSAHS were divided into 3 groups according to the severity of OSAHS. They underwent bilateral tonsillectomy plus adenoidectomy or adenoidectomy alone. Repeated polysomnography and integrated visual and auditory continuous performance testing (IVA-CPT) was performed to assess full-scale response control quotient (FRCQ), full-scale attention quotient (FAQ), and hyperactivity (HYP) before surgery and 3 and 6 months after surgery.ResultsMean FRCQ, FAQ, and HYP significantly improved over time in the 3 groups (FRCQ, F=292.05; FAQ, F=258.27; HYP, F=295.10, all P<0.001). FRCQ and HYP were not significantly different among the groups at the 3 time points. FAQ was significantly different among the groups (F=3.89, P<0.05). For FRCQ, FAQ, and HYP, there was no interaction between time and disease severity. Within groups, the effect of time on the apnea-hypopnea index (AHI) and lowest oxygen saturation (LaSO2) were significant for each group and they were significantly different among the 3 groups at each time point (all P<0.001).ConclusionsThese results suggest that OSAHS may have a significant impact on self-control, attention, and hyperactivity in children, which is gradually alleviated after surgery. Disease severity was not closely related to preoperative mental and psychological function or postoperative recovery. Thus, we find it difficult to determine the impact degree of OSAHS severity on mental and psychological function or predict postoperative recovery by using OSAHS severity alone in children.