Validation of snoring detection using a smartphone app

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

BACKGROUND

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.

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Mobile Technologies for Monitoring Sleep Time: A Systematic Search of App Stores in Brazil

Introduction  Decreased sleep time is detrimental to physical and mental health, exerting negative impacts on daily functioning and quality of life. Mobile health (mHealth) applications can be useful for improving sleep quality. Objective  To perform a systematic search of mHealth applications for monitoring sleep time at Brazilian online stores and evaluate the selected applications in terms of engagement, functionality, aesthetics, and quality of information. Methods  A systematic search was performed at the Google Play Store and the App Store developed or updated between 2020 and 2022. The mobile app rating scale (MARS) was used to assess their quality. The characteristics and information of these applications were presented. Data were analyzed descriptively. Results  Among the 2,264 relevant mHealth apps identified, 11 were eligible for inclusion. There was one app that offered only sleep time recordings, three offered sleep recordings and educational content, three monitored sleep time with audiovisual resources, two offered sleep time recordings with a focus on monitoring snoring, and two offered sleep time monitoring with individual content customizations. The apps scored fair on MARS, with a mean of 3.5 points (standard deviation: 0.8) on a scale of 1 to 5. Most apps scored low due to poor evidence-based information, aesthetics, and engagement. Conclusion  Although the use of mHealth technologies for monitoring sleep time has considerable potential for investigation, it is a relatively new, underexplored topic. Collaboration between researchers, clinical professionals, patients, and application developers is necessary for the creation of new evidence-based mHealth applications focused on Brazilian users.

Management of primary snoring in adults: A scoping review examining interventions, outcomes and instruments used to assess clinical effects

While various treatment options for primary snoring are available, evidence-based recommendations to determine the optimal intervention remain unestablished. To inform future directions of research to guide clinical decision-making, this scoping review was conducted to map the existing evidence on interventions for primary snoring, the outcomes and instruments used to assess their clinical effects in adults. The feasibility of conducting further systematic reviews and comparing outcomes across these therapies using network meta-analysis was also assessed. Of the 1673 records identified, 38 interventional studies met the inclusion criteria with three-fifths of them being before-after studies. The most common reason for study exclusion was results being reported for patients with primary snoring and obstructive sleep apnoea (OSA) combined. Interventions were surgical (73 %), behavioural and the use of devices/medications. Twenty-six common outcomes were identified and categorised into six domains. Fifty-nine instruments were used to assess the outcomes and based mainly on non-validated questionnaires. Our findings indicated (1) the need for randomised controlled trials with strict discrimination between patients with primary snoring and OSA, (2) further network meta-analyses using some outcomes is feasible, and (3) a core outcome set to inform standardised reporting for future research should be developed.

Validation of Downloadable Mobile Snore Applications by Polysomnography (PSG)

Purpose

Obstructive sleep apnea (OSA) is a common breathing disorder during sleep that is associated with symptoms such as snoring, excessive daytime sleepiness, and breathing interruptions. Polysomnography (PSG) is the most reliable diagnostic test for OSA; however, its high cost and lengthy testing duration make it difficult to access for many patients. With the availability of free snore applications for home-monitoring, this study aimed to validate the top three ranked snore applications, namely SnoreLab (SL), Anti Snore Solution (ASS), and Sleep Cycle Alarm (SCA), using PSG.

Patients and Methods

Sixty participants underwent an overnight PSG while simultaneously using three identical smartphones with the tested apps to gather sleep and snoring data.

Results

The study discovered that all three applications were significantly correlated with the total recording time and snore counts of PSG, with ASS showing good agreement with snore counts. Furthermore, the Snore Score, Time Snoring of SL, and Sleep Quality of SCA had a significant correlation with the natural logarithm of apnea hypopnea index (lnAHI) of PSG. The Snore Score of SL and the Sleep Quality of SCA were shown to be useful for evaluating snore severity and for pre-diagnosing or predicting OSA above moderate levels.

Conclusion

These findings suggest that some parameters of free snore applications can be employed to monitor OSA progress, and future research could involve adjusted algorithms and larger-scale studies to further authenticate these downloadable snore and sleep applications.

A multi-branch convolutional neural network for snoring detection based on audio

Obstructive sleep apnea (OSA) is associated with various health complications, and snoring is a prominent characteristic of this disorder. Therefore, the exploration of a concise and effective method for detecting snoring has consistently been a crucial aspect of sleep medicine. As the easily accessible data, the identification of snoring through sound analysis offers a more convenient and straightforward method. The objective of this study was to develop a convolutional neural network (CNN) for classifying snoring and non-snoring events based on audio. This study utilized Mel-frequency cepstral coefficients (MFCCs) as a method for extracting features during the preprocessing of raw data. In order to extract multi-scale features from the frequency domain of sound sources, this study proposes the utilization of a multi-branch convolutional neural network (MBCNN) for the purpose of classification. The network utilized asymmetric convolutional kernels to acquire additional information, while the adoption of one-hot encoding labels aimed to mitigate the impact of labels. The experiment tested the network's performance by utilizing a publicly available dataset consisting of 1,000 sound samples. The test results indicate that the MBCNN achieved a snoring detection accuracy of 99.5%. The integration of multi-scale features and the implementation of MBCNN, based on audio data, have demonstrated a substantial improvement in the performance of snoring classification.

High-Speed Network DDoS Attack Detection: A Survey

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber-Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

Dependable algorithm for visualizing snoring duration through acoustic analysis: A pilot study

Snoring is a nuisance for the bed partners of people who snore and is also associated with chronic diseases. Estimating the snoring duration from a whole-night sleep period is challenging. The authors present a dependable algorithm for visualizing snoring durations through acoustic analysis. Both instruments (Sony digital recorder and smartphone's SnoreClock app) were placed within 30 cm from the examinee's head during the sleep period. Subsequently, spectrograms were plotted based on audio files recorded from Sony recorders. The authors thereby developed an algorithm to validate snoring durations through visualization of typical snoring segments. In total, 37 snoring recordings obtained from 6 individuals were analyzed. The mean age of the participants was 44.6 ± 9.9 years. Every recorded file was tailored to a regular 600-second segment and plotted. Visualization revealed that the typical features of the clustered snores in the amplitude domains were near-isometric spikes (most had an ascending-descending trend). The recorded snores exhibited 1 or more visibly fixed frequency bands. Intervals were noted between the snoring clusters and were incorporated into the whole-night snoring calculation. The correlative coefficients of snoring rates from digitally recorded files examined between Examiners A and B were higher (0.865, P < .001) than those with SnoreClock app and Examiners (0.757, P < .001; 0.787, P < .001, respectively). A dependable algorithm with high reproducibility was developed for visualizing snoring durations.

Effect of myofunctional therapy on snoring in obese patients: a randomized trial

OBJECTIVE

To analyze the effectiveness of myofunctional therapy (MT) in the treatment of habitual snoring in obese patients.

MATERIAL AND METHODS

This randomized clinical trial consisted of an experimental group (n=14) that underwent MT and a control group (n=26) that performed nonspecific exercises for the treatment of snoring. The Epworth sleepiness scale (ESS), Pittsburgh sleep quality index (PSQI), and short-form health survey (SF-36) were applied before and after treatment. Snoring was assessed subjectively by asking the partner about improvement after treatment. The SnoreLab app was used for objective assessment.

RESULTS

There was no significant effect of MT on any of the SnoreLab variables analyzed when groups, time points or covariates (adherence, age, body mass index [BMI], neck circumference, and sex) were compared. Neck circumference (cm) and the Pittsburgh sleep quality index score were significantly higher after treatment. There was no change in the Epworth sleepiness scale score after treatment. A correlation was found between BMI and the Pittsburgh sleep quality index and between BMI and the functional capacity component of the SF-36. Patient adherence was similar between groups.

DISCUSSION

Apps for recording snoring are a useful tool to be explored. MT exerted no significant effect on habitual snoring in obese patients despite the reduction of the snore score in the experimental group. Therapy applied without exclusion criteria based on the severity of sleep breathing disorders and pharyngeal characteristics fails to achieve the results necessary to treat habitual snoring in obese patients.

Long-term benefits of a new oral appliance on adult snoring: a trend analysis

Background: Snoring constitutes a worldwide public health concern that may be associated with daytime fatigue, endothelial dysfunction, vascular injury, stroke, cardiovascular diseases, and diabetes among female patients. This study explored the effects of the so-called Lin Oral Appliance (LOA) on Taiwanese adults’ snoring rates.Methods: A time series analysis was conducted to examine the associations between LOAs’ tongue compressors of different lengths, and snoring rates were calculated using the SnoreClock app. The LOA comprises 2 components: custom-made dental braces and tongue compressors of adjustable lengths; different versions had different-length compressors.Results: Our multiple linear regression time-series model revealed the effects of the LOA on snoring rates. The results indicated the following: i) LOA tongue compressor lengths of 1 and 2.5 cm (LOA-1 and LOA-2.5, respectively) were associated with reduced snoring rates; ii) sleep durations of 5.5-7.5 h and daytime sleepiness were associated with increased snoring rates; and iii) among participants with snoring rates above 10%, the snoring rates observed 1-7 days before a given day constituted a significant factor influencing snoring rates on the given day.Conclusions: We discovered that the LOA could reduce snoring rates and that the 2.5-cm compressor length in the LOA produced the best results.

Effects of a novel oral appliance on snoring in adults: A pilot study

AbstractBackground/purpose

Oral appliances (OAs) have been recommended as alternatives for adult patients with obstructive sleep apnea who are intolerant of continuous positive airway pressure therapy. The aim of this study was to explore the effect on snoring rates among adult patients through use of a novel OA termed the Lin OA (LOA, airflow-interference-type nasal congestion relieving and snore-ceasing oral appliance).

Materials and methods

The LOA consist of two parts: dental braces and a fixed tongue compressor. The compressor lengths range from 0.5 cm to 3.5 cm across versions. Patients used the LOA during sleep and the SnoreClock smartphone application recorded their snoring rates.

Results

A total of 4920 recordings (4239 recordings from 34 men, 681 recordings from 8 women) were used for the analysis. The recordings were sorted in accordance with the applied length of the LOA tongue compressor (0.5–3.5 cm, LOA-0.5, LOA-1 and LOA-3.5), and participants not using the LOA were denoted as the LOA-0 group. The women had higher snoring rates in the LOA-0, LOA-0.5 to LOA-2 groups, but lower snoring rates in the LOA-3 group than men by the univariate analysis. The snoring rates were significantly reduced by a mean of 5.04% with every 1 cm increase in tongue compressor length. Continuous LOA use resulted in snoring rate reductions of 0.02% per day by the random intercept model of the linear regression.

Conclusion

Use of this novel LOA may significantly reduce snoring rates by 5.04% with each 1 cm increase in tongue compressor length.

Accuracy of a Smartphone Application Measuring Snoring in Adults-How Smart Is It Actually?

About 40% of the adult population is affected by snoring, which is closely related to obstructive sleep apnea (OSA) and can be associated with serious health implications. Commercial smartphone applications (apps) offer the possibility of monitoring snoring at home. However, the number of validation studies addressing snoring apps is limited. The purpose of the present study was to assess the accuracy of recorded snoring using the free version of the app SnoreLab (Reviva Softworks Ltd., London, UK) in comparison to a full-night polygraphic measurement (Miniscreen plus, Löwenstein Medical GmbH & Co., KG, Bad Ems, Germany). Nineteen healthy adult volunteers (4 female, 15 male, mean age: 38.9 ± 19.4 years) underwent simultaneous polygraphic and SnoreLab app measurement for one night at home. Parameters obtained by the SnoreLab app were: starting/ending time of monitoring, time in bed, duration and percent of quiet sleep, light, loud and epic snoring, total snoring time and Snore Score, a specific score obtained by the SnoreLab app. Data obtained from polygraphy were: starting/ending time of monitoring, time in bed, total snoring time, snore index (SI), snore index obstructive (SI obstructive) and apnea-hypopnea-index (AHI). For different thresholds of percentage snoring per night, accuracy, sensitivity, specificity, positive and negative predictive values were calculated. Comparison of methods was undertaken by Spearman-Rho correlations and Bland-Altman plots. The SnoreLab app provides acceptable accuracy values measuring snoring >50% per night: 94.7% accuracy, 100% sensitivity, 94.1% specificity, 66.6% positive prediction value and 100% negative prediction value. Best agreement between both methods was achieved in comparing the sum of loud and epic snoring ratios obtained by the SnoreLab app with the total snoring ratio measured by polygraphy. Obstructive events could not be detected by the SnoreLab app. Compared to polygraphy, the SnoreLab app provides acceptable accuracy values regarding the measurement of especially heavy snoring.