Simple Snoring Research Articles

Snoring and its Associated Comorbidities

Abstract Objective Snoring is often perceived as a simple social nuisance, whereas it can be a telltale sign of serious respiratory diseases. The present study aimed to determine the prevalence of self-reported habitual snoring and to identify its associated factors. Materials and Methods This cross-sectional study surveyed 815 individuals about their medical history, anthropometric characteristics, and lifestyle using a questionnaire. Results The prevalence of self-reported habitual snoring in our sample was 29.2%. Variables independently associated with snoring were advanced age (p = 0.008), asthma (p = 0.003), sleepiness (p < 0.001), hyperthyroidism (p = 0.006), smoking (p < 0.001), diabetes (p = 0.010), and abdominal obesity (p = 0.007). Conclusion This survey has highlighted the importance of snoring in an Arab-African context, to bring more awareness to this respiratory disorder and to understand that a simple snore can be a silent cry of an organism in need of help.

Effects of modified oropharyngeal exercises on individuals with simple snoring: An online randomized controlled trial.

Individuals who suffer from simple snoring rarely go to a doctor due to a lack of medical knowledge, but simple snoring can reduce the individual's quality of life and may cause social problems to the bed partner/family members. The aims of the present study are to explore the effects of online modified oropharyngeal exercises on the individuals with simple snoring and to provide a rehabilitation method for individuals with simple snoring. This study is a double-blinded, two-arm, randomized controlled trial. Participants were enrolled and randomly assigned to the intervention group (n = 33) or the control group (n = 33). The participants in the control group received health education on snoring, while the participants in the intervention group received the modified oropharyngeal exercise besides health education on snoring. The intervention duration was 4 weeks. The primary outcomes included snoring index, snoring loudness, and snoring quantity. The secondary outcomes included self-reported snoring, sleep quality, daytime sleepiness, anxiety symptoms, depression symptoms, and quality of life. All outcomes were measured at baseline, 4 weeks, and 8 weeks. Generalized estimating equations (GEE) analyses showed significant differences between the intervention group and the control group on snoring index, loudness, and quantity (p < .001). Moreover, modified oropharyngeal exercise had effects on sleep quality, daytime sleepiness, anxiety symptoms, depression symptoms, and quality of life in individuals with simple snoring (p < .001). Self-reported snoring also improved at 8 weeks. The modified oropharyngeal exercises were effective in improving simple snoring. It could also improve sleep quality, daytime sleepiness, anxiety symptoms, depression symptoms, and quality of life.

Cephalometric analysis with magnetic resonance imaging in patients with obstructive sleep apnea syndrome

Objectives: The study aimed to assess soft tissues and bony structures in patients with snoring and obstructive sleep apnea syndrome (OSAS) by magnetic resonance imaging (MRI) cephalometry and to investigate the correlation between the degree of OSAS with cephalometric measures. Patients and Methods: The prospective study included 137 adult patients (86 males, 51 females; mean age: 44.2±11.8; range 21 to 70 years) admitted with complaints of snoring or witnessed apnea between December 2020 and June 2023. Polysomnography and MRI cephalometry were performed for each patient. According to the polysomnography results, patients were assigned to one of the following groups: simple snorers [apnea-hypopnea index (AHI) &lt;5; n=25], mild OSAS (AHI between 5 and 15; n=37), moderate OSAS (AHI between 15 and 30; n=36), and severe OSAS (AHI &gt;30; n=39). The SNA (sella, nasion-point A), SNB (sella, nasion-point B), submental fat thickness, retropalatal and retro glossal distance, tongue, uvula, basion, and hyoid-related cephalometric measurements were evaluated with MRI cephalometry. Results: The H-A (hyoid-point A) values were found to be higher in the severe OSAS patients than in other groups (p&lt;0.001, p&lt;0.001 and p&lt;0.05, respectively). The PNS-UT (posterior nasal spine-uvula tip) values of the patients in the severe OSAS group were found to be higher than patients in both the snoring and moderate OSAS groups (p&lt;0.001). The SNB values of the patients in the mild OSAS group were found to be higher than the patients in the snoring group (p&lt;0.05). In the parameters of UW (uvula thickness), PAS-RG (posterior airway space-retroglossal distance), and H-B (hyoid-point B), statistically meaningful differences were found only between the simple snorers group and the group with severe OSAS. Conclusion: In OSAS patients, some important distances, angles, and lengths can be evaluated by avoiding ionizing radiation with MRI cephalometry.

SnoreLab Application in the Assessment of Obstructive Sleep Apnea Syndrome: A Pilot Study

Background: SnoreLab (Version 5.3, Reviva Softworks Ltd., London, UK) is featured as the number one mobile application (app) for measuring snoring intensity and duration. The aim of this study was to conduct a comparative analysis between the results derived from SnoreLab and polysomnography (PSG). Methods: Male and female patients between 18 and 75 years of age, seeking medical counseling for suspected obstructive sleep apnea syndrome (OSAS), were considered eligible to participate in this study. Exclusion criteria were psychological or neurological disorders, drug or alcohol abuse, and inability to follow instructions. All patients underwent one overnight in-hospital PSG with simultaneous snoring recording using the SnoreLab app. Results: Nineteen patients (15 men, 4 women) aged 50.9 ± 10.5 years were included. The overall cohort exhibited a Snore Score of 24.8 ± 22.2 alongside an AHI of 15.1 ± 17.0, indicating moderate OSAS. Interestingly, elevated Snore Scores were observed in both the simple snoring (30.7 ± 19.2) and severe OSAS group (35.2 ± 21.4) (Kruskal–Wallis p-value = 0.176). The analysis of the Spearman’s test did not reveal a statistically significant correlation between PSG parameters and the Snore Score. Conclusions: While SnoreLab records snoring, it is not designed for screening sleep apnea. Although SnoreLab may not replace PSG for use in diagnosis, it could serve as a complementary tool for monitoring snoring and to improve the interaction between patients and clinicians when integrated into a suitable clinical assessment.

Evaluation of adropin indices and PCSK9 in non-diabetic men with severe obstructive sleep apnea

Abstract Objectives We aimed to investigate the relationship among proprotein convertase subtilisin/kexin type 9 (PCSK9), adropin levels, inflammation, and sleep variables in non-diabetic males with severe obstructive sleep apnea (OSA). Methods This cross-sectional study included adults aged 18 to 65 who underwent polysomnography due to sleep problems between July 2019 and August 2020. Participants were grouped based on their apnea-hypopnea index (AHI). We included 32 males with simple snoring (AHI&lt;5 events/h) as the controls and 48 males with severe OSA (AHI≥30 events/h). Furthermore, patients with severe OSA were divided into two groups based on body mass index (BMI), resulting in three groups in total. Adropin and PCSK9 were analyzed using the enzyme-linked immunosorbent assay method. Results In severe OSA with BMI≥30 kg/m2, compared to the controls, blood pressure values, interleukin-6 (IL-6), white blood cell (WBC) count, systemic inflammation response index, neutrophil, monocyte counts were found to be higher, but adropin/BMI, adropin/waist circumference, adropin/neck circumference were significantly lower. Adropin/BMI had the highest correlation coefficient with IL-6. Although there was no significant difference in PCSK9 levels among the groups, PCSK9 was independently correlated with the WBC and its subsets. Conclusions Our study is of clinical importance as it is the first to show a relationship between PCSK9 and inflammation markers in severe OSA. Also, this study demonstrated the potential value of adropin, in combination with BMI, as a valuable indicator for assessing inflammation and OSA severity.

Differences and Risk Factors of Peripheral Blood Immune Cells in Patients with Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) is a respiratory disorder characterized by chronic intermittent hypoxia and fragmented sleep, leading to inflammatory response and oxidative stress. However, the differences in immune inflammatory response in OSA patients with different severity remain unclear. This study aims to examine the differences in peripheral blood immune cells and their risk factors in OSA patients. A total of 277 snoring patients from the Sleep Respiratory Disorder Monitoring Center of Zhongnan Hospital of Wuhan University were recruited in this study. According to the diagnosis and severity criteria of OSA, the included patients were further divided into simple snoring, mild, moderate, and severe groups. Peripheral blood immune cell counts including white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells, platelets, and polysomnography indicators were collected from the patients. Compared with simple snoring patients, the OSA patients had increased circular monocyte and basophil count levels. In addition, correlation analysis results indicated that monocyte count was positively associated with chronic obstructive pulmonary disease (COPD), smoking, apnea-hypopnea index (AHI), the longest apnea duration, and Oxygen desaturation index (ODI), and negatively correlated with average SpO2 in snoring patients. Finally, multiple linear regression analysis revealed that AHI, COPD, smoking, and maximum heart rate were independent predictors of monocyte count. OSA patients had a significant increase in their peripheral blood monocyte count. AHI, COPD, smoking, and maximum heart rate were risk factors for increased peripheral blood monocyte count in OSA patients. These findings suggest that peripheral blood monocytes can be considered an inflammatory biomarker of OSA.

Treatment of Obstructive Sleep Apnea and Simple Snoring: Efficacy of a New Mandibular Advancement Device

The following prospective study has the aim of evaluating the efficiency of the F22 MAD (mandibular advancement device), a new oral device for the treatment of OSAS (Obstructive Sleep Apnea Syndrome) and snoring. Methods: AHI (apnea-hypopnea index), ODI (Oxygen Desaturation Index), snoring percentage, time spent in the supine position, and Epworth Sleepiness Scale score were evaluated in 19 patients with snoring, mild to moderate OSAS, or severe OSAS who declined CPAP (Continuous Positive Air Pressure) treatment, before and after the application of the F22 MAD. Results: The median value of AHI varied from 15.6 ± 10.7 to 5.7 ± 5.7; the median value of ODI varied from 13.4 ± 8.8 to 6.2 ± 5.2; the median value of the percentage of snoring varied from 30.7 ± 7 to 7.5 ± 10.8, except for the patient who has severe OSAS who increased their value. The value obtained by the self-completion of the ESS questionnaire (Epworth Sleepiness Scale) underwent a statistically significant variation, while clinically significant for 13\19 patients who obtained a reduction of the value &gt;/= of 2 points. Conclusions: It is possible to conclude that the F22 MAD is effective in the treatment of patients with mild and moderate OSAS or simple snoring, reducing the polysomnographic outcomes with statistically and clinically significant results in terms of reduction of AHI, ODI and percentage of snoring.

Automatically detecting OSAHS patients based on transfer learning and model fusion

Objective. Snoring is the most typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) that can be used to develop a non-invasive approach for automatically detecting OSAHS patients. Approach. In this work, a model based on transfer learning and model fusion was applied to classify simple snorers and OSAHS patients. Three kinds of basic models were constructed based on pretrained Visual Geometry Group-16 (VGG16), pretrained audio neural networks (PANN), and Mel-frequency cepstral coefficient (MFCC). The XGBoost was used to select features based on feature importance, the majority voting strategy was applied to fuse these basic models and leave-one-subject-out cross validation was used to evaluate the proposed model. Main results. The results show that the fused model embedded with top-5 VGG16 features, top-5 PANN features, and MFCC feature can correctly identify OSAHS patients (AHI > 5) with 100% accuracy. Significance. The proposed fused model provides a good classification performance with lower computational cost and higher robustness that makes detecting OSAHS patients at home possible.

Correlation between serum leptin level and sleep monitoring indexes in patients with obstructive sleep apnea hypopnea syndrome and its predictive value: a cross-sectional analysis.

To investigate the association between serum leptin (LP) level and polysomnography (PSG) parameters in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). A cross-sectional study was conducted. The data of subjects who underwent PSG at hospital between January 2021 and December 2022 were collected retrospectively, 220 participants were included. The subjects were categorized into simple snoring group (n = 45), mild OSAHS group (n = 63), moderate OSAHS group (n = 52), and severe OSAHS group (n = 60). The general characteristics, PSG indices, and serological indices were collected retrospectively. Pearson correlation analysis was used to observe the correlation between serum LP level and PSG parameters. The value of serum LP level in predicting OSAHS was analyzed by receiver operating characteristic curve. The serum LP level was positively correlated with micro-arousal count, micro-arousal index (MAI), high apnea hypopnea index, times of blood oxygen decreased by≥3% and time in saturation lower 90%, and negatively correlated with lowest nocturnal oxygen saturation and mean oxygen saturation (p < 0.05). The area under the curve (AUC) of serum LP level in predicting the occurrence of OSAHS was 0.8276 (95% CI: 0.7713-0.8839), and when the Youden index was 0.587, the sensitivity was 72.00%, and the specificity was 86.67% (p < 0.0001). In the population with high MAI, the AUC of serum LP level in predicting the occurrence of OSAHS was 0.8825 (95% CI: 0.7833-0.9817), and when the Youden index was 0.690, the sensitivity was 79.00% and the specificity was 90.00% (p < 0.0001). Serum LP level is associated with the severity of OSAHS. Serum LP level demonstrates a strong predictive value for the occurrence of OSAHS, particularly in population with high MAI.

Changes of Serum Adiponectin Level in Patients with Obstructive Sleep Apnea Hypopnea Syndrome and Its Relationship with Sleep Monitoring Indexes.

To observe the changes of serum adiponectin (AP) levels in patients with obstructive sleep apnea hypopnea syndrome (OSAHS) and explore the correlation between serum AP and polysomnography (PSG) parameters in patients with OSAHS. The data of subjects who underwent PSG at the hospital between January 2021 and December 2022 were collected retrospectively and divided into simple snoring group (AHI < 5 times/h, n = 45), mild OSAHS group (5 ≤ AHI < 15 times/h, n = 63), moderate OSAHS group (15 ≤ AHI ≤ 30 times/h, n = 52), and severe OSAHS group (AHI > 30 times/h, n = 60). The general data, PSG indices, and serological indices of the subjects were collected and compared between groups. Pearson correlation analysis and partial correlation analysis were employed to examine the correlation between serum AP level and PSG parameters. Ordered logistic regression was employed to analyze the risk factors influencing the severity of OSAHS. The predictive capability of the serum AP level in determining the occurrence of OSAHS was assessed using ROC. The serum AP levels of subjects with different subtypes of PSG indicators were compared. In the simple snoring group, mild OSAHS group, moderate OSAHS group, and severe OSAHS group, there were statistically significant differences in microarousal count, MAI, AHI, times of blood oxygen decreased by ≥ 3%, L-SaO2, and TS90% among the 4 groups (P < 0.05). The level of serum AP was positively correlated with L-SaO2 and negatively correlated with the proportion of REM, microarousal count, MAI, AHI, times of blood oxygen decreased by ≥ 3%, TS90%, and LP (P < 0.05). High AHI was a risk factor affecting the severity of OSAHS (95% CI: 1.446-4.170). The AUC of serum AP level in diagnosing OSAHS was 0.906 (95% CI: 0.8601-0.9521), and when the Youden Index was 0.678, the sensitivity was 88.9%, and the specificity was 78.9% (P < 0.0001). In the population with a high microarousal count, high AHI, and high times of blood oxygen decreased by ≥ 3% and high TS90%, the serum AP level was lower than that in the low-level population (P < 0.05). In the population with high L-SaO2, the serum AP level was higher than that in low-level population (P < 0.05). The level of serum AP decreased with the increase of the disease severity of patients with OSAHS and demonstrates a significant predictive capability for the occurrence of OSAHS. Monitoring the level of serum AP can effectively forecast the risk of OSAHS. Furthermore, alterations in serum AP levels are associated with both hypoxemia and a heightened frequency of arousal in patients with OSAHS.

Modifications in Upper Airway Collapsibility during Sleep Endoscopy with a Mandibular Positioner: Study in Snorers and Obstructive Sleep Apnea Patients.

Mandibular advancement devices (MADs) are an effective treatment for patients with sleep-related breathing disorders, with variable response. Increasingly more research points to the predictive value of Drug-Induced Sleep Endoscopy (DISE) in patient selection. This study aims to analyze the changes in upper airway collapsibility using a titratable MAD simulator during DISE. This study included 104 patients with simple snoring and obstructive sleep apnea (OSA). The VOTE scale was used to assess the presence of collapses during the DISE both without and with the MAD simulator. In snorers, there was a decrease in collapses at the level of the soft palate and oropharynx when the advancement was achieved. Patients with mild OSA also showed a decrease in collapses at the base of the tongue. Patients with moderate/severe OSA exhibited significant amelioration at all levels. The levels at which there were residual collapses despite the maneuver were, in order, the velopharynx, oropharynx, epiglottis, and tongue. The MAD simulator reduces collapsibility at all levels and in all severity groups. Residual collapses suitable for combined treatments were able to be identified. This highlights the need for individualized patient selection, as upper airway collapsibility exhibits variable improvement or worsening with the MAD simulator regardless of the severity of the condition.

A deep learning algorithm model to automatically score and grade obstructive sleep apnea in adult polysomnography.

Polysomnography (PSG) is unique in diagnosing sleep disorders, notably obstructive sleep apnea (OSA). Despite its advantages, manual PSG data grading is time-consuming and laborious. Thus, this research evaluated a deep learning-based automated scoring system for respiratory events in sleep-disordered breathing patients. A total of 1000 case PSG data were enrolled to develop a deep learning algorithm. Of the 1000 data, 700 were distributed for training, 200 for validation, and 100 for testing. The respiratory events scoring deep learning model is composed of five sequential layers: an initial layer of perceptrons, followed by three consecutive layers of long short-term memory cells, and ultimately, an additional two layers of perceptrons. The PSG data of 100 patients (simple snoring, mild, moderate, and severe OSA; n = 25 in each group) were selected for validation and testing of the deep learning model. The algorithm demonstrated high sensitivity (95% CI: 98.06-98.51) and specificity (95% CI: 95.46-97.79) across all OSA severities in detecting apnea/hypopnea events, compared to manual PSG analysis. The deep learning model's area under the curve values for predicting OSA in apnea-hypopnea index ≥ 5, 15, and 30 groups were 0.9402, 0.9388, and 0.9442, respectively, showing no significant differences between each group. The deep learning algorithm employed in our study showed high accuracy in identifying apnea/hypopnea episodes and assessing the severity of OSA, suggesting the potential for enhancing both the efficiency and accuracy of automated respiratory event scoring in PSG through advanced deep learning techniques.

Correlation analysis of cardiopulmonary exercise test indices and conditions of overweight patients with obstructive sleep apnea: a retrospective study.

The cardiopulmonary function of patients with obstructive sleep apnea (OSA) is significantly lower than that of patients with simple snoring and is significantly related to the severity of OSA. Currently, only a few studies have been conducted on cardiopulmonary exercise testing in overweight patients with OSA. To analyze the correlation between cardiopulmonary exercise test (CPET) indices and the condition of overweight patients with OSA. Retrospective study in Guangdong Provincial Hospital of Chinese Medicine. This study included 73 hospitalized overweight patients. The patients were divided into no, mild, moderate, and severe OSA groups. Differences in the CPET indices among the four groups were compared. The correlation between the CPET indices and conditions was analyzed. No, mild, moderate, and severe OSA groups had 18 men and 5 women, 11 men and 3 women, 12 men and 2 women, and 21 men and 1 woman, respectively (P>0.05). No significant difference was observed in resting pulmonary function among the four groups (P>0.05). In the CPET, the anaerobic threshold, maximum oxygen uptake, and oxygen pulse were significantly lower in the severe OSA group than those in the normal OSA group (P<0.05). Moreover, CPET indices negatively correlated with the apnea-hypopnea index. Changes in CPET indices occurred earlier than changes in resting pulmonary function in patients with OSA. CPET might be a potential method for evaluating the severity of OSA combined with overweight status.

Analysis of dynamic functional connectivity states and influencing factors of brain network in male patients with obstructive sleep apnea

Objective: To analyze dynamic functional connectivity (dFNC) states and influencing factors of brain network in male patients with obstructive sleep apnea (OSA). Methods: A total of 111 male patients diagnosed with obstructive sleep apnea or presenting with simple snoring, who visited the Sleep Clinic at the Second Affiliated Hospital of Soochow University between August 2020 and December 2021, were prospectively selected for this study. General information was collected, and polysomnography (PSG) was performed. Based on the oxygen desaturation index (ODI), the participants were divided into three groups: primary snoring group (ODI<5 events/hour, n=34), mild to moderate OSA group (5 events/hour≤ODI<30 events/hour, n=43), and sever OSA group (ODI≥30 events/hour, n=34). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale, and daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS). Resting-state functional magnetic resonance imaging (fMRI) data were collected and preprocessed. dFNC matrices were constructed using a sliding time window approach. The number of dFNC states was determined using k-means clustering analysis. Three parameters, namely, fractional time (FT), mean dwell time (MDT), and number of transitions (NT), were used to characterize the temporal properties of dFNC states. Differences in the temporal properties of dFNC states among the groups were compared. The correlations between temporal properties and PSG parameters, as well as MoCA and ESS scores, were further analyzed. Multiple stepwise linear regression analysis was performed to identify the influencing factors of the temporal properties of dFNC states. Results: The age of the patients was (40.2±8.6) years (range: 25-65 years). There were no significant differences in age, smoking history and alcohol history, and MoCA scores among the three groups (all P>0.05). Three dFNC states were extracted through k-means clustering analysis: state 1, characterized by strong connections within the visual and sensorimotor networks with a frequency of 31.7% (4 611/14 541); state 2, characterized by strong connections within the default mode network, attention network, and other cognitive networks, with the lowest frequency of 22.1% (3 213/14 541); state 3, characterized by weaker connections across the whole brain, with the highest frequency of 46.2% (6 717/14 541). The FT [0.28 (0.05, 0.35) vs 0.39 (0.26, 0.53)] and MDT [8.20 (4.35, 12.54) vs 11.68 (8.50, 16.69)] of state 2 in the sever OSA group were lower than those in the primary snoring group (both P<0.05), while there were no significant differences in the temporal properties of states 1 and 3 among the three groups (all P>0.05). The FT and MDT of state 2 were correlated with body mass index (BMI), apnea-hypopnea index (AHI), ODI, and minimum oxygen saturation (MinSaO2) (FT: r values were -0.218, -0.230, -0.249, 0.198, respectively; MDT: r values were 0.269, -0.253, -0.265, 0.209, respectively; all P<0.05). There were no significant correlations between the temporal properties and MoCA or ESS scores (all P>0.05). ODI was found to be an influencing factor for the temporal properties of state 2 (FT: β=-0.225, 95%CI:-0.227 to -0.223; MDT: β=-0.241, 95%CI:-0.289 to -0.195). Conclusions: Male patients with OSA exhibit alterations in specific temporal properties of brain network dynamic functional connectivity, which are associated with nocturnal oxygen parameters. This may be one of the mechanisms underlying brain functional damage in patients with OSA.

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

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

Habitual Snoring in Pediatric Thalassemia Disease; Prevalence, Quality of Life and Risk Factors

Objective: To compare the prevalence of HS and quality of life in non-transfusion dependent thalassemia (NTDT) and Transfusion dependent thalassemia (TDT) patients and to identify risk factors associated with HS in pediatric thalassemia. Materials and Methods: We conducted a cross-sectional study of pediatric thalassemic patients aged from 6 months - 18 years between January 2020 and October 2020, at Thammasat University Hospital, Thailand. Results: There were 141 thalassemia patients (35 TDT and 106 NTDT), aged 7 months-18 years, 73 (51.8%) were male. Sixty-eight patients (48.2%) reported snoring; 28 patients (19.9%) had HS; the remaining 40 patients (28.4%) had simple snoring. The prevalence of HS was not significantly different between TDT and NTDT group (6 (17.1%) VS 22 (20.8%); P= 0.527). Quality of life assessed by OSA-18 score was not significant difference between TDT and NTDT groups (51.3 ± 18.8 VS 45.7 ± 11.4; P=0.141). The associating risk factors for the development of HS after multivariate logistic analysis were nasal congestion, and male gender, with an adjusted OR of 5.3 and 3.0, respectively. Conclusion: Prevalence of HS was increased in children with thalassemia. Factors such as nasal congestion and male gender were strongly associated with HS in this population. The quality of life assessment using the OSA-18 questionnaire indicated that thalassemia children generally exhibited a good quality of life. Additionally, our study observed relatively low serum ferritin levels in comparison to previous studies. The standard care provided for TDT patients, includes regular blood transfusion and effective iron chelation, may contribute to slowing down the degree of nasopharyngeal narrowing in thalassemia patients.

A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring

Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.

AHI estimation of OSAHS patients based on snoring classification and fusion model

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a chronic and common sleep-breathing disease that could negatively influence lives of patients and cause serious concomitant diseases. Polysomnography(PSG) is the gold standard for diagnosing OSAHS, but it is expensive and requires overnight hospitalization. Snoring is a typical symptom of OSAHS. This study proposes an effective OSAHS screening method based on snoring sound analysis. Snores were labeled as OSAHS related snoring sounds and simple snoring sounds according to real-time PSG records. Three models were used, including acoustic features combined with XGBoost, Mel-spectrum combined with convolution neural network (CNN), and Mel-spectrum combined with residual neural network (ResNet). Further, the three models were fused by soft voting to detect these two types of snoring sounds. The subject's apnea-hypopnea index (AHI) was estimated according to these recognized snoring sounds. The accuracy and recall of the proposed fusion model achieved 83.44% and 85.27% respectively, and the predicted AHI has a Pearson correlation coefficient of 0.913 (R2 = 0.834, p < 0.001) with PSG. The results demonstrate the validity of predicting AHI based on analysis of snoring sound and show great potential for monitoring OSAHS at home.

The use of the CT90 value in predicting anxiety in OSA: could it be a useful parameter?

Obstructive sleep apnea (OSA) is characterized by recurrent episodes of complete or partial obstruction of the upper airway leading to episodic desaturation. OSA patients often show symptoms of anxiety. Our study aimed to examine the presence and levels of anxiety in OSA and simple snoring relative to control subjects and to investigate the correlation between anxiety scores and polysomnographic, demographic, and sleepiness parameters.The study included 80 OSA, 30 simple snoring, and 98 control cases. Demographic, anxiety, and sleepiness data of all subjects were acquired. The Beck Anxiety Inventory (BAI) was used to determine the level of anxiety. The Epworth Sleepiness Scale (ESS) was used to evaluate the sleepiness level of participants. In addition, polysomnography recordings of those in the OSA and the simple snoring group were acquired.Significantly higher anxiety scores were found in patients with obstructive sleep apnea and simple snoring compared to the control group (p<0.01, p<0.01, respectively). From the polysomnographic data obtained from OSA and simple snoring subjects, the CT90 values (cumulative percentage of the time spent at saturations below 90%) and the AHI showed a weak positive correlation between the level of anxiety (p=0.004, r=0.271; p=0.04, r=0.196, respectively).Our study concluded that polysomnographic data showing the depth and duration of hypoxia may be more reliable in showing neuropsychological disorder and hypoxia-related comorbidities in OSA. The CT90 value can be used as a measure in the assessment of anxiety in OSA. Its advantage is that it can be measured with overnight pulse oximetry along with in-laboratory PSG and HSAT (home sleep apnea test).

Clinical Review of Commercially-Available Anti-Snoring Devices

Snoring is a common issue that negatively affects sleep quality. Generally, simple snorers without sleep apnea may be managed with various therapeutic options such as behavioral treatments, mandibular advancement devices or surgeries, while more complicated snorers with moderate to severe sleep apnea may be managed with positive airway pressure. Recently, commercially-available devices have been widely implemented to improve snoring by widening the nasal airway or preventing mouth breathing. Nasal dilator strips are typically used as external nasal dilators, while internal nasal dilators include nasal stents, nasal clips, and nasal stimulators. Chin straps and mouth tape can be used to improve adherence to positive airway pressure, as well as to manage simple snorers with mouth breathing. However, there is a scarcity of information in literature with regard to the clinical role of these devices. Therefore, physicians should carefully consider whether these commercially-available devices can be a viable alternative management option for snoring patients. Herein, we provide literature reviews of clinically-available devices for snorers with their indication, effectiveness, and safety.