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1.
Respir Res ; 25(1): 224, 2024 May 29.
Article in English | MEDLINE | ID: mdl-38811937

ABSTRACT

The soft palate and back of the throat represent vulnerable early infection sites for SARS-CoV-2, influenza, streptococci, and many other pathogens. We demonstrate that snoring causes aerosolization of pharyngeal fluid that covers these surfaces, which previously has escaped detection because the inspired airstream carries the micron-sized droplets into the lung, inaccessible to traditional aerosol detectors. While many of these droplets will settle in the lower respiratory tract, a fraction of the respirable smallest droplets remains airborne and can be detected in exhaled breath. We distinguished these exhaled droplets from those generated by the underlying breathing activity by using a chemical tracer, thereby proving their existence. The direct transfer of pharyngeal fluids and their pathogens into the deep lung by snoring represents a plausible mechanistic link between the previously recognized association between sleep-disordered breathing and pneumonia incidence.


Subject(s)
Sleep Apnea Syndromes , Snoring , Humans , Snoring/diagnosis , Snoring/physiopathology , Sleep Apnea Syndromes/diagnosis , Sleep Apnea Syndromes/physiopathology , Male , Female , Aerosols , COVID-19 , Adult , Pneumonia/metabolism , Pneumonia/diagnosis , Middle Aged , Pharynx/microbiology
2.
Physiol Meas ; 45(5)2024 May 23.
Article in English | MEDLINE | ID: mdl-38722551

ABSTRACT

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.


Subject(s)
Sleep Apnea, Obstructive , Humans , Sleep Apnea, Obstructive/diagnosis , Sleep Apnea, Obstructive/physiopathology , Automation , Male , Neural Networks, Computer , Middle Aged , Adult , Female , Signal Processing, Computer-Assisted , Snoring/diagnosis , Snoring/physiopathology
3.
BMC Prim Care ; 25(1): 110, 2024 Apr 08.
Article in English | MEDLINE | ID: mdl-38589791

ABSTRACT

BACKGROUND: Children Snoring is a common childhood disorder that affects the growth and development of children and is detrimental to their health. Increasing awareness of Children Snoring among parents is important. AIM: To develop the Knowledge-Attitude-Practice of Parents towards Children Snoring Scale and test the reliability and validity of the scale. METHODS: The development of the tool was divided into two phases involving 1257 parents from China. In the first phase, an initial project bank was created through a literature review. This was followed by a Delphi expert consultation, group discussion and pre-survey. The second stage screened the items and conducted an exploratory factor analysis, then conducted a confirmatory factor analysis and tested for reliability and validity. RESULTS: Support was found for the 25-item Knowledge-Attitude-Practice toward Children Snoring scale. Exploratory and confirmatory factor analyses provide support for four subscales: (parental basic cognition toward Children Snoring; parents' perception of complications of Children Snoring; parents' attitude towards Children Snoring; parents' concern and prevention of Children Snoring). Internal consistency for the total scale was high (Cronbach's α = 0.93). The intraclass correlation coefficient of test-retest reliability was 0.92 (95%CI: 0.85 to 0.95), which provided support for the stability of the scale. CONCLUSION: The Knowledge-Attitude-Practice of Parents towards Children Snoring scale shows promise as a measure that may be used by medical workers and community children's health managers.


Subject(s)
Parents , Snoring , Child , Humans , Reproducibility of Results , Snoring/diagnosis , Attitude , China
5.
Physiol Meas ; 45(3)2024 Mar 11.
Article in English | MEDLINE | ID: mdl-38316023

ABSTRACT

Objective.Obstructive sleep apnea (OSA) is a high-incidence disease that is seriously harmful and potentially dangerous. The objective of this study was to develop a noncontact sleep audio signal-based method for diagnosing potential OSA patients, aiming to provide a more convenient diagnostic approach compared to the traditional polysomnography (PSG) testing.Approach.The study employed a shifted window transformer model to detect snoring audio signals from whole-night sleep audio. First, a snoring detection model was trained on large-scale audio datasets. Subsequently, the deep feature statistical metrics of the detected snore audio were used to train a random forest classifier for OSA patient diagnosis.Main results.Using a self-collected dataset of 305 potential OSA patients, the proposed snore shifted-window transformer method (SST) achieved an accuracy of 85.9%, a sensitivity of 85.3%, and a precision of 85.6% in OSA patient classification. These values surpassed the state-of-the-art method by 9.7%, 10.7%, and 7.9%, respectively.Significance.The experimental results demonstrated that SST significantly improved the noncontact audio-based OSA diagnosis performance. The study's findings suggest a promising self-diagnosis method for potential OSA patients, potentially reducing the need for invasive and inconvenient diagnostic procedures.


Subject(s)
Sleep Apnea, Obstructive , Snoring , Humans , Snoring/diagnosis , Polysomnography , Sleep Apnea, Obstructive/diagnosis
6.
IEEE Trans Biomed Eng ; 71(2): 494-503, 2024 Feb.
Article in English | MEDLINE | ID: mdl-37616136

ABSTRACT

Snoring is a prominent characteristic of sleep-disordered breathing, and its detection is critical for determining the severity of the upper airway obstruction and improving daily quality of life. Home snoring analysis is a highly invasive method, but it becomes challenging when a sleeping partner also snores, leading to distorted evaluations in such environments. In this article, we tackle the problem of complex snore signal separation of multiple snorers. This article introduces two audio-based methods that efficiently extract an individual's snoring signal, allowing for the analysis of sleep-breathing disorders in a normal sleeping environment without isolating individuals. In the first method, Principal Component Analysis (PCA) identifies the source components from the finite number of modes generated by the decomposition of the snoring mixture using Multivariate Variational Mode Decomposition (MVMD). The second method applies Blind Source Separation (BSS) based on Non-Negative Matrix Factorization (NMF) to separate the single-channel snoring mixture. Furthermore, the decomposed signals are tuned using the iterative enhancement algorithm to adequately match the source snoring signals. These methods were evaluated by simulating various real-time snoring recordings of 7 subjects (2 men, 2 women, and 3 children). The correlation coefficient between the source and its separated signal was computed to assess the separation results, exhibiting good performance of the methods used. The enhancement approach also demonstrated its efficiency by increasing the correlation over to 80% in both methods. The experimental results show that the proposed algorithms are effective and practical for separating mixed snoring signals.


Subject(s)
Sleep Apnea Syndromes , Snoring , Male , Child , Humans , Female , Snoring/diagnosis , Quality of Life , Sleep Apnea Syndromes/diagnosis , Sleep , Algorithms
7.
Ann Am Thorac Soc ; 21(1): 114-121, 2024 Jan.
Article in English | MEDLINE | ID: mdl-37879037

ABSTRACT

Rationale: The physiological factors modulating the severity of snoring have not been adequately described. Airway collapse or obstruction is generally the leading determinant of snore sound generation; however, we suspect that ventilatory drive is of equal importance. Objective: To determine the relationship between airway obstruction and ventilatory drive on snore loudness. Methods: In 40 patients with suspected or diagnosed obstructive sleep apnea (1-98 events/hr), airflow was recorded via a pneumotachometer attached to an oronasal mask, ventilatory drive was recorded using calibrated intraesophageal diaphragm electromyography, and snore loudness was recorded using a calibrated microphone attached over the trachea. "Obstruction" was taken as the ratio of ventilation to ventilatory drive and termed flow:drive, i.e., actual ventilation as a percentage of intended ventilation. Lower values reflect increased flow resistance. Using 165,063 breaths, mixed model analysis (quadratic regression) quantified snore loudness as a function of obstruction, ventilatory drive, and the presence of extreme obstruction (i.e., apneic occlusion). Results: In the presence of obstruction (flow:drive = 50%, i.e., doubled resistance), snore loudness increased markedly with increased drive (+3.4 [95% confidence interval, 3.3-3.5] dB per standard deviation [SD] change in ventilatory drive). However, the effect of drive was profoundly attenuated without obstruction (at flow:drive = 100%: +0.23 [0.08-0.39] dB per SD change in drive). Similarly, snore loudness increased with increasing obstruction exclusively in the presence of increased drive (at drive = 200% of eupnea: +2.1 [2.0-2.2] dB per SD change in obstruction; at eupneic drive: +0.14 [-0.08 to 0.28] dB per SD change). Further, snore loudness decreased substantially with extreme obstruction, defined as flow:drive <20% (-9.9 [-3.3 to -6.6] dB vs. unobstructed eupneic breathing). Conclusions: This study highlights that ventilatory drive, and not simply pharyngeal obstruction, modulates snore loudness. This new framework for characterizing the severity of snoring helps better understand the physiology of snoring and is important for the development of technologies that use snore sounds to characterize sleep-disordered breathing.


Subject(s)
Sleep Apnea Syndromes , Sleep Apnea, Obstructive , Humans , Snoring/diagnosis , Polysomnography/methods , Sound
8.
J Clin Sleep Med ; 20(1): 85-92, 2024 Jan 01.
Article in English | MEDLINE | ID: mdl-37707290

ABSTRACT

STUDY OBJECTIVES: Airway inflammation in patients with obstructive sleep apnea (OSA) has been described and can be assessed by measuring the biomarker fractional exhaled nitric oxide (FeNO). In this pilot study, we investigated FeNO measurements in identification of OSA among persons with snoring. METHODS: In this study we aimed to investigate (1) if FeNO could be used in screening for OSA, (2) if daytime sleepiness correlated to FeNO levels, and (3) whether asthma affected FeNO levels. Persons with snoring were prospectively included in three primary care ear, nose, and throat clinics. Patients underwent spirometry, FeNO tests, and partial polygraphy. They filled out questionnaires on sinonasal and asthma symptoms, daytime sleepiness, and quality of life. Current smokers, patients with upper airway inflammatory conditions, and patients treated with steroids were excluded. RESULTS: Forty-nine individuals were included. Median apnea-hypopnea index was 11.4, mean age was 50.9 years, and 29% were females. OSA was diagnosed in 73% of the patients of whom 53% had moderate-severe disease. Patients with moderate-severe OSA had significantly higher FeNO counts than patients with no or mild OSA (P = .024). Patients younger than 50 years with a FeNO below 15 had the lowest prevalence of moderate-severe OSA. No correlation was found between FeNO measurements and daytime sleepiness, and asthma did not affect FeNO levels. CONCLUSIONS: We found a low prevalence of moderate-severe OSA in persons with snoring when FeNO and age were low. This might be considered in a future screening model, though further studies testing the FeNO cutoff level and the diagnostic accuracy are warranted. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: NO Measurements in Screening for Asthma and OSA, in Patients With Severe Snoring; URL: https://clinicaltrials.gov/study/NCT03964324; Identifier: NCT03964324. CITATION: Kiaer E, Ravn A, Jennum P, et al. Fractional exhaled nitric oxide-a possible biomarker for risk of obstructive sleep apnea in snorers. J Clin Sleep Med. 2024;20(1):85-92.


Subject(s)
Asthma , Disorders of Excessive Somnolence , Sleep Apnea, Obstructive , Female , Humans , Middle Aged , Male , Fractional Exhaled Nitric Oxide Testing , Snoring/complications , Snoring/diagnosis , Snoring/therapy , Quality of Life , Pilot Projects , Sleep Apnea, Obstructive/complications , Sleep Apnea, Obstructive/diagnosis , Biomarkers , Asthma/complications , Asthma/diagnosis , Disorders of Excessive Somnolence/diagnosis
9.
Phys Eng Sci Med ; 47(1): 99-108, 2024 Mar.
Article in English | MEDLINE | ID: mdl-37878092

ABSTRACT

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a serious chronic sleep disorder. Snoring is a common and easily observable symptom of OSAHS patients. The purpose of this work is to identify OSAHS patients by analyzing the acoustic characteristics of snoring sounds throughout the entire night. Ten types of acoustic features, such as Mel-frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC) and spectral entropy among others, were extracted from the snoring sounds. A fused feature selection algorithm based on ReliefF and Max-Relevance and Min-Redundancy (mRMR) was proposed for optimal feature set selection. Four types of machine learning models were then applied to validate the effectiveness of OSAHS patient identification. The results show that the proposed feature selection algorithm can effectively select features with high contribution, including MFCC and LPC. Based on the selected top-20 features and using a support vector machine model, the accuracies in identifying OSAHS patients under the thresholds of AHI = 5,15, and 30, were 100%, 100%, and 98.94%, respectively. This indicates that the proposed model can effectively identify OSAHS patients.


Subject(s)
Sleep Apnea, Obstructive , Snoring , Humans , Snoring/diagnosis , Polysomnography , Sleep Apnea, Obstructive/diagnostic imaging , Sleep , Syndrome
10.
Nutr Metab Cardiovasc Dis ; 33(12): 2334-2343, 2023 Dec.
Article in English | MEDLINE | ID: mdl-37788950

ABSTRACT

BACKGROUNDS AND AIMS: Evidence on the association between habitual snoring, excessive daytime sleepiness (EDS), and cardiovascular diseases (CVDs) remains uncertain and limited. The study aimed to explore the independent and joint association between habitual snoring, EDS, and CVDs in rural Chinese adults. METHODS AND RESULTS: A total of 28,140 participants from the Henan rural cohort study were included. Sleep status information was obtained by self-reported. Based on their sleep status, the participants were classified into four groups: "no snoring and no EDS (NSNS) (reference group)", "snoring and no EDS (SNS)", "no snoring and EDS (NSS)", "snoring and EDS (SS)." The logistic regression models were used to calculate independent and joint odds ratios (OR) and 95% confidence intervals (CI) between the snoring, EDS status and stroke, CHD, and CVD. Of the 28,140 participants, 740 subjects reported snoring and sleepiness. The ORs and (95% CIs) for CVDs in the adjusted model were 1.31 (1.20-1.43) for participants who snored frequently and 2.44 (1.76-3.39) for frequent sleepiness compared with no snoring and no sleepiness. Individuals with both snoring and sleepiness had higher odds of CVDs compared with no snoring and no sleepiness (OR: 2.18, 95%CI: 1.80-2.62). CONCLUSION: Habitual snoring and excessive daytime sleepiness were independently and jointly associated with CVDs in the Chinese rural population. More studies are needed to explore the mechanisms of the relationship. CLINICAL TRIAL REGISTRATION: The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). Date of registration: 2015-52 07-06. http://www.chictr.org.cn/showproj.aspx?proj=11375.


Subject(s)
Cardiovascular Diseases , Disorders of Excessive Somnolence , Humans , Adult , Cardiovascular Diseases/diagnosis , Cardiovascular Diseases/epidemiology , Snoring/diagnosis , Snoring/epidemiology , Cohort Studies , Rural Population , Sleepiness , East Asian People , Disorders of Excessive Somnolence/diagnosis , Disorders of Excessive Somnolence/epidemiology
11.
Sci Rep ; 13(1): 14009, 2023 08 28.
Article in English | MEDLINE | ID: mdl-37640790

ABSTRACT

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.


Subject(s)
Sleep Apnea Syndromes , Sleep Apnea, Obstructive , Humans , Snoring/diagnosis , Neural Networks, Computer , Algorithms , Sleep Apnea, Obstructive/diagnosis
12.
J Clin Pediatr Dent ; 47(4): 25-34, 2023 Jul.
Article in English | MEDLINE | ID: mdl-37408343

ABSTRACT

Orofacial myofunctional disorders (OMD) and sleep-disordered breathing (SDB) may present as comorbidities. Orofacial characteristics might serve as a clinical marker of SDB, allowing early identification and management of OMD and improving treatment outcomes for sleep disorders. The study aims to characterize OMD in children with SDB symptoms and to investigate possible relationships between the presence of various components of OMD and symptoms of SDB. A cross-sectional study of healthy children aged 6-8 from primary schools was conducted in central Vietnam in 2019. SDB symptoms were collected using the parental Pediatric Sleep Questionnaire, Snoring Severity Scale, Epworth Daytime Sleepiness Scale, and lip-taping nasal breathing assessment. Orofacial myofunctional evaluation included assessment of tongue mobility, as well as of lip and tongue strength using the Iowa Oral Performance Instrument, and of orofacial characteristics by the protocol of Orofacial Myofunctional Evaluation with Scores. Statistical analysis was used to investigate the relationship between OMD components and SDB symptoms. 487 healthy children were evaluated, of whom 46.2% were female. There were 7.6% of children at high risk of SDB. Children with habitual snoring (10.3%) had an increased incidence of restricted tongue mobility and decreased lip and tongue strength. Abnormal breathing patterns (22.4%) demonstrated lower posterior tongue mobility and lower muscle strength. Daytime sleepiness symptoms were associated with changes in muscle strength, facial appearance, and impaired orofacial function. Lower strengths of lip and tongue or improper nasal breathing were more likely to be present in children with reported sleep apnea (6.6%). Neurobehavioral symptoms of inattention and hyperactivity were linked to anomalous appearance/posture, increases in tongue mobility and oral strength. This study demonstrates a prevalence of orofacial myofunctional anomalies in children exhibiting SDB symptoms. Children with prominent SDB symptoms should be considered as candidates for further orofacial myofunctional assessment.


Subject(s)
Disorders of Excessive Somnolence , Sleep Apnea Syndromes , Humans , Child , Female , Male , Snoring/diagnosis , Snoring/epidemiology , Cross-Sectional Studies , Sleep Apnea Syndromes/diagnosis , Sleep Apnea Syndromes/epidemiology , Surveys and Questionnaires
13.
Physiol Meas ; 44(8)2023 08 14.
Article in English | MEDLINE | ID: mdl-37506712

ABSTRACT

Objective.Sleep apnea has a high incidence and is a potentially dangerous disease, and its early detection and diagnosis are challenging. Polysomnography (PSG) is considered the best approach for sleep apnea detection, but it requires cumbersome and complicated operations. Thus, it cannot satisfy the family healthcare needs.Approach.To facilitate the initial detection of sleep apnea in the home environment, we developed a sleep apnea classification model based on snoring and hybrid neural network, and implemented the well trained model in an embedded hardware platform. We used snore signals from 32 patients at Shenzhen People's Hospital. The Mel-Fbank features were extracted from snore signals to build a sleep apnea classification model based on Bi-LSTM with attention mechanism.Main results.The proposed model classified snore signals into four types: hypopnea, normal condition, obstructive sleep apnea, and central sleep apnea, with 83.52% and 62.31% accuracies, corresponding to the subject-dependence and subject-independence validation, respectively. After pruning and model quantization, at the cost of 0.81% and 0.95% accuracy loss of the subject dependence and subject independence classification, respectively, the number of model parameters and model storage space were reduced by 32.12% and 60.37%, respectively. The model exhibited accuracies of 82.71% and 61.36% based on the subject dependence and subject independence validations, respectively. When the well trained model was successfully porting and running on an STM32 ARM-embedded platform, the model accuracy was 58.85% for the four classifications based on leave-one-subject-out validation.Significance.The proposed sleep apnea detection model can be used in home healthcare for the initial detection of sleep apnea.


Subject(s)
Sleep Apnea, Obstructive , Snoring , Humans , Snoring/diagnosis , Pilot Projects , Sleep Apnea, Obstructive/diagnosis , Polysomnography/methods
14.
Am J Otolaryngol ; 44(5): 103964, 2023.
Article in English | MEDLINE | ID: mdl-37392727

ABSTRACT

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.


Subject(s)
Sleep Apnea, Obstructive , Snoring , Humans , Snoring/diagnosis , Snoring/etiology , Polysomnography/methods , Sleep , Syndrome
15.
Compend Contin Educ Dent ; 44(6): 320-324, 2023 Jun.
Article in English | MEDLINE | ID: mdl-37418468

ABSTRACT

For non-obstructive sleep apnea diagnosed patients with predominantly palatal snoring, Elevoplasty® is an efficient, minimally invasive treatment option. Aimed at reducing snoring severity, the innovative procedure involves the placement of three to four small resorbable polydioxanone barbed sutures, which are buried in the tissues of the soft palate. After placement, the sutures are "activated" by a gentle pull, which provides a "lift" of the soft palatal tissues and uvula. The soft palate, thus, is moved off of the posterior pharyngeal tissues at the back of the throat, providing an increased opening of the posterior pharyngeal airway and a reduction in snoring severity. This article provides an overview of this procedure along with other treatments for snoring.


Subject(s)
Sleep Apnea, Obstructive , Snoring , Humans , Snoring/surgery , Snoring/diagnosis , Sleep Apnea, Obstructive/surgery , Palate, Soft/surgery , Uvula/surgery , Minimally Invasive Surgical Procedures
16.
Eur Arch Otorhinolaryngol ; 280(8): 3783-3789, 2023 Aug.
Article in English | MEDLINE | ID: mdl-37027027

ABSTRACT

PURPOSE: The influence of adenoidectomy ± tonsillotomy/tonsillectomy on objective sleep parameters in children with Obstructive Sleep Apnea (OSA) was determined with the help of ambulatory polygraphy (WatchPat300®, Neucomed Ltd., Vienna, Austria). These results were compared with the findings of the OSA-18 questionnaire. METHODS: 27 children treated with adenoidectomy ± tonsillotomy/tonsillectomy at the Department of Otorhinolaryngology, Head and Neck Surgery, Medical University of Innsbruck, were consecutively included in this prospective clinical trial. Pre- and postoperative objective sleeping parameters were assessed with outpatient polygraphy (WatchPat300®) and subjective symptoms with the OSA-18 questionnaire. RESULTS: Most of the children presented with severe OSA (41%, 11/27). The mean preoperative AHI was 10.2 (± 7.4). Postoperatively it declined to 3.7 (± 1.8; p < 0.0001). Following surgery 19/24 (79%) children had a mild OSA and 8/24 (21%) a moderate OSA. None of the children suffered from severe OSA anymore after surgery. The postoperative AHI did not correlate with the age (p = 0.3), BMIp (p = 0.6) or extent of surgery (p = 0.9). The mean postoperative OSA-18 survey score was significantly lower than the preoperative one (70.7 ± 26.7 vs. 34.5 ± 10.5; p < 0.0001). The postoperative OSA-18 questionnaire showed a normal survey score below 60 in 23/24 (96%) of the children. CONCLUSIONS: The WatchPat® device might be a feasible way for objective assessment of pediatric OSA in children older than 3 years. Adenoidectomy ± tonsillotomy/tonsillectomy caused a significant decrease of the AHI in children with OSA. This effect was especially pronounced in children with severe OSA and none of the children had persistent severe OSA after surgery.


Subject(s)
Sleep Apnea Syndromes , Sleep Apnea, Obstructive , Tonsillectomy , Child , Child, Preschool , Humans , Adenoidectomy/methods , Feasibility Studies , Quality of Life , Sleep , Sleep Apnea Syndromes/complications , Sleep Apnea, Obstructive/diagnosis , Sleep Apnea, Obstructive/surgery , Sleep Apnea, Obstructive/etiology , Snoring/diagnosis , Snoring/etiology , Snoring/surgery , Tonsillectomy/methods , Prospective Studies
17.
IEEE J Biomed Health Inform ; 27(7): 3129-3140, 2023 Jul.
Article in English | MEDLINE | ID: mdl-37058373

ABSTRACT

Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation.


Subject(s)
Deep Learning , Wearable Electronic Devices , Humans , Polysomnography , Snoring/diagnosis , Apnea , Sleep
18.
Rev Neurol ; 76(9): 279-285, 2023 05 01.
Article in Spanish | MEDLINE | ID: mdl-37102252

ABSTRACT

INTRODUCTION: Obstructive sleep apnoea syndrome (OSAS) affects between 1% and 6% of children. Its diagnosis includes: a) snoring and/or apnoea; and b) an apnoea and hypopnoea index >3/hour obtained by polysomnography (PSG). The main aim of this work is to determine the prevalence of OSAS in our study population. PATIENTS AND METHODS: We conducted a descriptive study with a sample of 151 children aged between 1 and 12 years, who had been referred to the sleep unit of the Hospital General Universitario Gregorio Maranon for a PSG. We analysed the demographic variables sex and age; the clinical variables snoring, apnoeas and tonsillar hypertrophy; and the presence of OSAS based on the polysomnographic diagnostic criterion of an apnoea and hypopnoea index >3/hour. RESULTS: The mean age of the sample was 5.37 years (standard deviation: 3.05) and 64.9% were males. In 90.1% of cases, the reason for the visit was suspected OSAS. Snoring, apnoeas and tonsillar hypertrophy were observed in 73.5, 48.7 and 60% of cases, respectively. OSAS was diagnosed en 19 children (12.6%); in 13.5% of snorers; in 15.1% of those with apnoeas; and in 15.6% of the children with tonsillar hypertrophy. CONCLUSIONS: In our study, the prevalence of OSAS in children was 12.6%, which is higher than that reported in most epidemiological studies that include PSG for the diagnosis of OSAS.


TITLE: Prevalencia del síndrome de apnea obstructiva del sueño infantil en una unidad de sueño de referencia.Introducción. El síndrome de apnea obstructiva del sueño (SAOS) afecta a entre el 1 y el 6% de la población infantil. En su diagnóstico, se incluyen: a) ronquidos y/o apneas; y b) un índice de apneas e hipopneas >3/hora obtenido en la polisomnografía (PSG). El objetivo principal de este trabajo es determinar la prevalencia del SAOS en nuestra población de estudio. Pacientes y métodos. Estudio descriptivo con una muestra de 151 niños con edades comprendidas entre 1 y 12 años, remitidos a la unidad de sueño del Hospital General Universitario Gregorio Marañón para la realización de una PSG. Se analizaron las variables demográficas sexo y edad; las variables clínicas ronquidos, apneas e hipertrofia amigdalar; y la presencia de SAOS basada en el criterio diagnóstico polisomnográfico de un índice de apneas e hipopneas >3/hora. Resultados. La edad media de la muestra fue de 5,37 años (desviación estándar: 3,05) y el 64,9% eran varones. En el 90,1% de los casos, el motivo de consulta fue sospecha de SAOS. Los ronquidos, las apneas y la hipertrofia amigdalar se observaron en el 73,5, el 48,7 y el 60% de los casos, respectivamente. Se diagnosticó SAOS en 19 (12,6%) niños; en el 13,5% de los roncadores; en el 15,1% de los niños con apneas; y en el 15,6% de los niños con hipertrofia amigdalar. Conclusiones. En nuestro estudio, la prevalencia del SAOS en niños fue del 12,6%, superior a la descrita en la mayoría de los estudios epidemiológicos, pero similar a la observada en los que incluyen la PSG para el diagnóstico del SAOS.


Subject(s)
Sleep Apnea, Obstructive , Snoring , Male , Child , Humans , Infant , Child, Preschool , Female , Snoring/epidemiology , Snoring/diagnosis , Prevalence , Sleep Apnea, Obstructive/diagnosis , Sleep Apnea, Obstructive/epidemiology , Referral and Consultation , Hypertrophy , Sleep
19.
BMC Anesthesiol ; 23(1): 126, 2023 04 17.
Article in English | MEDLINE | ID: mdl-37069514

ABSTRACT

BACKGROUND: The incidence of hypoxemia during painless gastrointestinal endoscopy remains a matter of concem. To date, there is no recognized simple method to predict hypoxemia in digestive endoscopic anesthesia. The NoSAS (neck circumference, obesity, snoring, age, sex) questionnaire, an objective and simple assessment scale used to assess obstructive sleep apnea (OSA), combined with the modified Mallampati grade (MMP), may have certain screening value. This combination may allow anesthesiologists to anticipate, manage, and consequently decrease the occurrence of hypoxemia. METHODS: This study was a prospective observational trial. The primary endpoint was the incidence of hypoxaemia defined as pulse oxygen saturation (SpO2) < 95% for 10 s. A total of 2207 patients admitted to our hospital for painless gastrointestinal endoscopy were studied. All patients were measured for age, height, weight, body mass index, neck circumference, snoring, MMP, and other parameters. Patients were divided into hypoxemic and non-hypoxemic groups based on the SpO2. The ROC curve was plotted to evaluate the screening value of the NoSAS questionnaire separately and combined with MMP for hypoxemia. The total NoSAS score was evaluated at cut-off points of 8 and 9. RESULTS: With a NoSAS score ≥ 8 as the critical value for analysis, the sensitivity for hypoxemia was 58.3%, the specificity was 88.4%, and the area under the ROC was 0.734 (P < 0.001, 95% CI: 0.708-0.759). With a NoSAS score ≥ 9 as a critical value, the sensitivity for hypoxemia was 36.50%, the specificity rose to 96.16%, and the area under the ROC was 0.663 (P < 0.001, 95% CI: 0.639-0.688). With the NoSAS Score combined with MMP for analysis, the sensitivity was 78.4%, the specificity was 84%, and the area under the ROC was 0.859 (P < 0.001, 95%CI:0.834-0.883). CONCLUSIONS: As a new screening tool, the NoSAS questionnaire is simple, convenient, and useful for screening hypoxemia. This questionnaire, when paired withMMP, is likely to be helpful for the screening of hypoxemia.


Subject(s)
Anesthesia , Snoring , Humans , Snoring/diagnosis , Snoring/etiology , Polysomnography/adverse effects , Hypoxia/diagnosis , Hypoxia/complications , Surveys and Questionnaires , Endoscopy, Gastrointestinal/adverse effects , Anesthesia/adverse effects
20.
Physiol Meas ; 44(4)2023 05 03.
Article in English | MEDLINE | ID: mdl-37059109

ABSTRACT

Objective.Snoring is a typical symptom of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS). In this study, an effective OSAHS patient detection system based on snoring sounds is presented.Approach.The Gaussian mixture model (GMM) is proposed to explore the acoustic characteristics of snoring sounds throughout the whole night to classify simple snores and OSAHS patients respectively. A series of acoustic features of snoring sounds of are selected based on the Fisher ratio and learned by GMM. Leave-one-subject-out cross validation experiment based on 30 subjects is conducted to validation the proposed model. There are 6 simple snorers (4 male and 2 female) and 24 OSAHS patients (15 male and 9 female) investigated in this work. Results indicates that snoring sounds of simple snorers and OSAHS patients have different distribution characteristics.Main results.The proposed model achieves average accuracy and precision with values of 90.0% and 95.7% using selected features with a dimension of 100 respectively. The average prediction time of the proposed model is 0.134 ± 0.005 s.Significance.The promising results demonstrate the effectiveness and low computational cost of diagnosing OSAHS patients using snoring sounds at home.


Subject(s)
Sleep Apnea, Obstructive , Snoring , Humans , Male , Female , Snoring/diagnosis , Polysomnography/methods , Sleep Apnea, Obstructive/diagnosis , Acoustics
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