Acoustic analysis of snoring sounds recorded with a smartphone according to obstruction site in OSAS patients

Airway morphology, hyoid position, and serum inflammatory markers of obstructive sleep apnea in children treated with modified twin-block appliances

OBJECTIVE

To investigate the effects of modified twin-block appliances (MTBA) on obstructive sleep apnea (OSA) and mandibular retrognathia and the changes in the upper airway, hyoid bone position, and hypoxia-related inflammatory marker levels in children with OSA.

METHODS

This study included children with OSA and mandibular retrognathia and those with class I without mandibular retrognathia (n = 35 each). The experimental group comprised children with OSA and mandibular retrognathia managed using MTBA. Postoperative and preoperative polysomnography, lateral cephalometric radiographs, and peripheral blood samples were collected. The control group comprised children with class I without mandibular retrognathia.

RESULTS

Following MTBA management, the experimental group exhibited decreased apnea-hypopnea index and increased lowest arterial oxygen saturation level (P < 0.05). Sella- and subspinale-nasion-supramental angles significantly increased and decreased, respectively (P < 0.05). Posterior soft palatal-posterior pharyngeal wall distance, apical palatal-middle pharyngeal wall distance, posterior airway space, epiglottis valley-hypopharyngeal wall distance, hyoid-prevertebral plane distance, and distance from the superior anterior point of the hyoid bone to the inferior anterior point of the third cervical spine significantly increased, whereas distance from the superior anterior point of the hyoid bone to the mandibular plane decreased (P < 0.05).

CONCLUSIONS

Children with OSA (n = 35) were managed using MTBA, which relieved the mandibular retrognathia deformity, widened the upper airway space, moved the hyoid bone forward and upward, and improved the sleep monitoring indicators. Thus, MTBA can achieve satisfactory therapeutic effect in children with OSA and mandibular retrognathia with mandibular advancement.

A flexible catheter-based sensor array for upper airway soft tissues pressure monitoring

Obstructive sleep apnea is a globally prevalent concern with significant health impacts, especially when coupled with comorbidities. Accurate detection and localization of airway obstructions are crucial for effective diagnosis and treatment, which remains a challenge for traditional sleep monitoring methods. Here, we report a catheter-based flexible pressure sensor array that continuously monitors soft tissue pressure in the upper airway and facilitates at the millimeter level. The sensor's design and versatile 3D femtosecond laser fabrication process enable adaptation to diverse materials and applications. In vitro testing demonstrates high sensitivity (38.1 Ω/mmHg) and excellent stability. The sensor array effectively monitors distributed airway pressure and accurately identifies obstructions in an obstructive sleep apnea animal model. In this work, we highlight the potential of this catheter-based sensor array for long-term, continuous upper airway pressure monitoring and its prospective applications in other medical devices for pressure measurement in human body cavities.

Palatal Morphology After Treatment of Children With Obstructive Sleep Apnoea Using the Modified Twin-Block Appliance

OBJECTIVE

To investigate changes in the upper maxillary palates of children with obstructive sleep apnoea (OSA) and mandibular retraction who were treated using modified twin-block appliances (MTBAs).

METHODS

Thirty-five OSA children (age: 6-12 years) with mandibular retraction were included as the experimental group and 35 children who were Angle's class I but without mandibular retraction were included as the control group. The experimental group was treated with MTBA. Plaster models were made before the treatment and at the end of the 6-month treatment period. Plaster models of the control group were made at inclusion and after 6 months. Some plaster models were excluded because of damage or their failure to exhibit sufficiently clear marks, which left 26 pairs each for the experimental and control groups. The gender and age of the experimental group were matched with those of the control group at the end of the treatment. Three-dimensional (3D) digital model information was gathered using the external oral scanning model, and the data were extracted and analysed statistically to clarify the morphologic improvement in the maxillopalatine in OSA children treated using MTBAs.

RESULTS

After the OSA children with mandibular retraction were treated with MTBAs, the maxillary intercusp width, intermolar width, anterior palate width, posterior palate width, and surface area and volume of the maxillary palate significantly increased (*P < .05). By contrast, the anterior palatal height-apex level (H3) significantly decreased (*P < .05).

CONCLUSIONS

Compared with the control group, the narrow maxillary arch and basal bone were significantly enlarged after the OSA children with mandibular retraction were treated using MTBAs. The palatal surface area and volume increased, thereby allowing more space for accommodating the tongue and relieving transverse dissonance of the dentition.

Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs

Brachycephalic obstructive airway syndrome (BOAS) is a highly prevalent respiratory disease affecting popular short-faced dog breeds such as Pugs and French bulldogs. BOAS causes significant morbidity, leading to poor exercise tolerance, sleep disorders and a shortened lifespan. Despite its severity, the disease is commonly missed by owners or disregarded by veterinary practitioners. A key clinical sign of BOAS is stertor, a low-frequency snoring sound. In recent years, a functional grading scheme has been introduced to semi-objectively grade BOAS based on the presence of stertor and other abnormal signs. However, correctly grading stertor requires significant experience and adding an objective component would aid accuracy and repeatability. This study proposes a recurrent neural network model to automatically detect and grade stertor in laryngeal electronic stethoscope recordings. The model is developed using a novel dataset of 665 labelled recordings taken from 341 dogs with diverse BOAS clinical signs. Evaluated via nested cross validation, the neural network predicts the presence of clinically significant BOAS with an area under the receiving operating characteristic of 0.85, an operating sensitivity of 71% and a specificity of 86%. The algorithm could enable widespread screening for BOAS to be conducted by both owners and veterinarians, improving treatment and breeding decisions.

How to objectively measure snoring: a systematic review

PURPOSE

Snoring is the most common symptom of obstructive sleep apnea. Various objective methods of measuring snoring are available, and even if the measurement is performed the same way, communication is difficult because there are no common reference values between the researcher and clinician with regard to intensity and frequency, among other variables. In other words, no consensus regarding objective measurement has been reached. This study aimed to review the literature related to the objective measurement of snoring, such as measurement devices, definitions, and device locations.

METHODS

A literature search based on the PubMed, Cochrane, and Embase databases was conducted from the date of inception to April 5, 2023. Twenty-nine articles were included in this study. Articles that mentioned only the equipment used for measurement and did not include individual details were excluded from the study.

RESULTS

Three representative methods for measuring snoring emerged. These include (1) a microphone, which measures snoring sound; (2) piezoelectric sensor, which measures snoring vibration; and (3) nasal transducer, which measures airflow. In addition, recent attempts have been made to measure snoring using smartphones and applications.

CONCLUSION

Numerous studies have investigated both obstructive sleep apnea and snoring. However, the objective methods of measuring snoring and snoring-related concepts vary across studies. Consensus in the academic and clinical communities on how to measure and define snoring is required.

New acoustic monitoring system quantifying aspiration risk during monitored anaesthesia care

Respiratory monitoring is crucial during monitored anaesthesia care (MAC) to ensure patient safety. Patients undergoing procedures like gastrointestinal endoscopy and dental interventions under MAC have a heightened risk of aspiration. Despite the risks, no current system or device can evaluate aspiration risk. This study presents a novel acoustic monitoring system designed to detect fluid retention in the upper airway during MAC. We conducted a prospective observational study with 60 participants undergoing dental treatment under MAC. We utilized a prototype acoustic monitoring system to assess fluid retention in the upper airway by analysing inspiratory sounds. Water was introduced intraorally in participants to simulate fluid retention; artificial intelligence (AI) analysed respiratory sounds pre and post-injection. We also compared respiratory sounds pre-treatment and during coughing events. Coughing was observed in 14 patients during MAC, and 31 instances of apnoea were detected by capnography. However, 27 of these cases had breath sounds. Notably, with intraoral water injection, the Stridor Quantitative Value (STQV) significantly increased; furthermore, the STQV was substantially higher immediately post-coughing in patients who coughed during MAC. In summary, the innovative acoustic monitoring system using AI provides accurate evaluations of fluid retention in the upper airway, offering potential to mitigate aspiration risks during MAC.Clinical trial number: jRCTs 062220054.

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.

Technologies in Home-Based Digital Rehabilitation: Scoping Review

BACKGROUND

Due to growing pressure on the health care system, a shift in rehabilitation to home settings is essential. However, efficient support for home-based rehabilitation is lacking. The COVID-19 pandemic has further exacerbated these challenges and has affected individuals and health care professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet clients' needs and ease the growing pressure on the health care system, it is necessary to provide an overview of existing, relevant, and future solutions shaping the constantly evolving market of technologies for home-based DR.

OBJECTIVE

In this scoping review, we aimed to identify digital technologies for home-based DR, predict new or emerging DR trends, and report on the influences of the COVID-19 pandemic on DR.

METHODS

The scoping review followed the framework of Arksey and O'Malley, with improvements made by Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO, and the Cochrane Library. The search spanned January 2015 to January 2022. A bibliometric analysis was performed to provide an overview of the included references, and a co-occurrence analysis identified the technologies for home-based DR. A full-text analysis of all included reviews filtered the trends for home-based DR. A gray literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic on the development of DR.

RESULTS

A total of 2437 records were included in the bibliometric analysis and 95 in the full-text analysis, and 40 records were included as a result of the gray literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital and mobile apps are already used in home-based DR; however, artificial intelligence and machine learning, exoskeletons, and digital and mobile apps represent new and emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches but has not led to the development of new technologies.

CONCLUSIONS

Multiple tools are available and implemented for home-based DR; however, some technologies face limitations in the application of home-based rehabilitation. However, artificial intelligence and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the health care system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients' needs and adhere to framework conditions, regardless of exceptional situations such as the COVID-19 pandemic.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Objectives

Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features.

Methods

We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening.

Results

The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk.

Conclusions

The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

Usefulness of new acoustic respiratory sound monitoring with artificial intelligence for upper airway assessment in obese patients during monitored anesthesia care

Monitored anesthesia care (MAC) often causes airway complications, particularly posing an elevated risk of aspiration and airway obstruction in obese patients. This study aimed to quantify the levels of aspiration and airway obstruction using an artificial intelligence (AI)-based acoustic analysis algorithm, assessing its utility in identifying airway complications in obese patients. To verify the correlation between the stridor quantitative value (STQV) calculated by acoustic analysis and body weight, and to further evaluate fluid retention and airway obstruction, STQV calculated exhaled breath sounds collected at the neck region, was compared before and after injection of 3 ml of water in the oral cavity and at the start and end of the MAC procedures. STQV measured immediately following the initiation of MAC exhibited a weak correlation with body mass index. Furhtermore, STQV values before and after water injection increased predominantly after injection, further increased at the end of MAC. AI-based analysis of cervical respiratory sounds can enhance the safety of airway management during MAC by quantifying airway obstruction and fluid retention in obese patients. J. Med. Invest. 70 : 430-435, August, 2023.

Predicting upper airway collapse sites found in drug-induced sleep endoscopy from clinical data and snoring sounds in patients with obstructive sleep apnea: a prospective clinical study

STUDY OBJECTIVES

The primary aim was to predict upper airway collapse sites found in drug-induced sleep endoscopy (DISE) from demographic, anthropometric, clinical examination, sleep study, and snoring sound parameters in patients with obstructive sleep apnea (OSA). The secondary aim was to identify the above-mentioned parameters that are associated with complete concentric collapse of the soft palate.

METHODS

All patients with OSA who underwent DISE and simultaneous snoring sound recording were enrolled in this study. Demographic, anthropometric, clinical examination (viz., modified Mallampati classification and Friedman tonsil classification), and sleep study parameters were extracted from the polysomnography and DISE reports. Snoring sound parameters during DISE were calculated.

RESULTS

One hundred and nineteen patients with OSA (79.8% men; age = 48.1 ± 12.4 years) were included. Increased body mass index was found to be associated with higher probability of oropharyngeal collapse (P < .01; odds ratio = 1.29). Patients with a high Friedman tonsil score were less likely to have tongue base collapse (P < .01; odd ratio = 0.12) and epiglottic collapse (P = .01; odds ratio = 0.20) than those with a low score. A longer duration of snoring events (P = .05; odds ratio = 2.99) was associated with a higher probability of complete concentric collapse of the soft palate.

CONCLUSIONS

Within the current patient profile and approach, given that only a limited number of predictors were identified, it does not seem feasible to predict upper airway collapse sites found in DISE from demographic, anthropometric, clinical examination, sleep study, and snoring sound parameters in patients with OSA.

CITATION

Huang Z, Bosschieter PFN, Aarab G, et al. Predicting upper airway collapse sites found in drug-induced sleep endoscopy from clinical data and snoring sounds in obstructive sleep apnea patients: a prospective clinical study. J Clin Sleep Med. 2022;18(9):2119-2131.

Determining the Site of Upper Airway Narrowing in Snorers Using a Noninvasive Technique

Background In this study, we aimed to determine the site of obstruction if surgical treatment is considered. Flexible nasopharyngoscopy is an invasive procedure currently used for the assessment of snoring and the level of obstruction. Here, we examine the role of Somnoscreen™ plus, a noninvasive cardiorespiratory polysomnographic device, in identifying the site of obstruction in patients presenting with snoring. Methodology This cross-sectional study was conducted in the Sleep Research Center at King Abdulaziz University Hospital. Polysomnography was conducted using Somnoscreen™ plus. All participants underwent flexible nasopharyngoscopy after polysomnography. Results Nasopharyngoscopy revealed that the most common site of obstruction was the nose and the soft palate (35.4%), followed by the soft palate alone (25%). Somnoscreen revealed that the site of obstruction was the nose and the soft palate in 18 (37.5%) patients and the nose alone in 16 (33.3%) patients. However, distal obstructions were not detected using Somnoscreen. The concordance of nasopharyngoscopy and Somnoscreen was 52.9%. However, it showed a discrepancy in identifying distal obstructions, which Somnoscreen™ plus failed to detect. Conclusions Somnoscreen appears to be sensitive for identifying proximal airway obstructions. The audio signal recordings can potentially be used as a tool to detect the site of airway obstruction in snoring; however, further studies are needed.

The Efficacy of Twin-Block Appliances for the Treatment of Obstructive Sleep Apnea in Children: A Systematic Review and Meta-Analysis

Objective

To evaluate the efficacy of twin-block appliance in the treatment of children with obstructive sleep apnea (OSA).

Methods

Two independent reviewers conducted a systematic review of seven databases from database establishment until October 16, 2021. There were no language restrictions. The outcomes were changes in apnea-hypopnea index (AHI), oxyhemoglobin desaturation index (ODI), and lowest arterial oxygen saturation (lowest SaO2). National Institute for Health and Clinical Excellence (NICE) tool was used to assess the quality of the studies included.

Results

A total of 207 articles were screened for relevance, and 6 of them met the inclusion criteria for our meta-analysis. Four of the studies were case series, 1 was nonrandomized control trial, and 1 was a randomized crossover clinical trial. After twin-block therapy, there was a significant decrease in AHI (4.35 events/hour, 95% CI: 4.04, 4.66, p ≤ 0.001). The lowest SaO2 significantly increased by 9.17% (95% CI: 12.05, 6.28, p ≤ 0.001). Sensitivity analysis by excluding studies one by one showed stable and favorable results in lowest SaO2 and AHI.

Conclusions

Results from the meta-analysis showed that the use of twin-block appliance significantly decreased AHI and significantly increased lowest SaO2. Hence, twin-block appliance therapy may be an effective method for the treatment of pediatric OSA. Further large sample size randomized controlled trials are needed to assess this treatment efficacy in children with obstructive sleep apnea.

Evaluation of cognitive, mental, and sleep patterns of post-acute COVID-19 patients and their correlation with thorax CT

Objective

In this study, we have evaluated the cognitive, mental, and sleep patterns of post-COVID patients 2 months after their hospitalization, and after scoring their hospitalization thorax CTs, we have compared the degree of the lung involvement with cognitive and mental states of the patients.

Materials and methods

Forty post-COVID patients were included in our study. Patients who were hospitalized due to COVID-19 and who had thorax CT scan at the admission were included in the study. Thorax CT scans of the patients were scored using chest severity scoring (CT-SS). The Mini-Mental State Examination test (MMSE), the Montreal Cognitive Assessment Test (MoCA), the Pittsburgh Sleep Quality Index, and the Hamilton Depression and Hamilton Anxiety scales of all the participants were evaluated by the same person.

Results

Early stage cognitive impairment was detected in 15% of post-COVID patients in the MMSE test and mean MMSE test score was 26.9 ± 2.1. The MoCA test detected cognitive impairment in 55% of the patients, and the mean MoCA score was 19.6 ± 5.2. Furthermore, all patients showed depressive symptoms in Hamilton Depression Scoring System and 57.5% of the patients showed anxiety symptoms in the Hamilton Anxiety Scoring System. The mean Pittsburg Sleep Quality Index of the patients was 10.7 ± 3.1, and it was found to be higher than normal. The mean CT-SS scores, which used to evaluate the lung involvement, of the patients were 4.7 ± 5.6. We did not find any correlation between patients’ cognitive tests and CT-SS scores.

Conclusion

When these results are taken into consideration, our study has shown that the neuropsychiatric symptoms of the patients who had COVID-19 continued even after 2 months of their illness. Therefore, long-term rehabilitation of these patients, including cognitive education and psychological services, should be continued.

Prediction of the obstruction sites in the upper airway in sleep-disordered breathing based on snoring sound parameters: a systematic review

BACKGROUND

Identification of the obstruction site in the upper airway may help in treatment selection for patients with sleep-disordered breathing. Because of limitations of existing techniques, there is a continuous search for more feasible methods. Snoring sound parameters were hypothesized to be potential predictors of the obstruction site. Therefore, this review aims to i) investigate the association between snoring sound parameters and the obstruction sites; and ii) analyze the methodology of reported prediction models of the obstruction sites.

METHODS

The literature search was conducted in PubMed, Embase.com, CENTRAL, Web of Science, and Scopus in collaboration with a medical librarian. Studies were eligible if they investigated the associations between snoring sound parameters and the obstruction sites, and/or reported prediction models of the obstruction sites based on snoring sound.

RESULTS

Of the 1016 retrieved references, 28 eligible studies were included. It was found that the characteristic frequency components generated from lower-level obstructions of the upper airway were higher than those generated from upper-level obstructions. Prediction models were built mainly based on snoring sound parameters in frequency domain. The reported accuracies ranged from 60.4% to 92.2%.

CONCLUSIONS

Available evidence points toward associations between the snoring sound parameters in the frequency domain and the obstruction sites in the upper airway. It is promising to build a prediction model of the obstruction sites based on snoring sound parameters and participant characteristics, but so far snoring sound analysis does not seem to be a viable diagnostic modality for treatment selection.

Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach

BACKGROUND

Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of cases. It can be diagnosed using several methods such as polysomnography.

OBJECTIVES

To overcome the challenges of time and cost faced by conventional diagnostic methods, this paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived from the upper-airway morphology with automatic segmentation using deep learning.

METHOD

We adopted a 3D UNet deep-learning model to perform medical image segmentation. 3D UNet prevents the feature-extraction loss that may occur by concatenating layers and extracts the anteroposterior coordination and width of the airway morphology. To create flow characteristics of the upper airway training data, we analyzed the changes in flow characteristics according to the upper-airway morphology using CFD. A multivariate Gaussian process regression (MVGPR) model was used to train the flow characteristic values. The trained MVGPR enables the prompt prediction of the aerodynamic features of the upper airway without simulation. Unlike conventional regression methods, MVGPR can be trained by considering the correlation between the flow characteristics. As a diagnostic step, a support vector machine (SVM) with predicted aerodynamic and biometric features was used in this study to classify patients as healthy or suffering from moderate OSAS. SVM is beneficial as it is easy to learn even with a small dataset, and it can diagnose various flow characteristics as factors while enhancing the feature via the kernel function. As the patient dataset is small, the Monte Carlo cross-validation was used to validate the trained model. Furthermore, to overcome the imbalanced data problem, the oversampling method was applied.

RESULT

The segmented upper-airway results of the high-resolution and low-resolution models present overall average dice coefficients of 0.76±0.041 and 0.74±0.052, respectively. Furthermore, the classification accuracy, sensitivity, specificity, and F1-score of the diagnosis algorithm were 81.5%, 89.3%, 86.2%, and 87.6%, respectively.

CONCLUSION

The convenience and accuracy of sleep apnea diagnosis are improved using deep learning and machine learning. Further, the proposed method can aid clinicians in making appropriate decisions to evaluate the possible applications of OSAS.

Spanish Society of Pulmonology and Thoracic Surgery positioning on the use of telemedine in sleep-disordered breathing and mechanical ventilation

The rapid introduction of new information and communication technologies into medical practice has prompted Spanish Society of Pulmonology and Thoracic SurgeryR to publish a position paper on sleep-disordered breathing, especially in relation to positive pressure treatment. It should be pointed out that the scientific literature is to some extent controversial due to a paucity of large randomized multicenter studies with long-term follow-up. Moreover, the telematics devices and systems on the market vary widely. As a result, the recommendations are based primarily on a consensus of expert professionals. Another very important aspect addressed extensively in this document is the obvious lack of regulations on legal matters and the operations of commercial companies. The most important recommendations included in this position paper are that telemedicine is primarily advocated in subjects with travel problems or who live far from the hospital, in patients with poor CPAP compliance, and in most cases treated with non-invasive mechanical ventilation. A key element is patient individualization. It is imperative that the relevant technical, legal and ethical requirements (medical device regulations, data protection, and informed consent) are met. Finally, expert professionals from our society must contribute to and become involved in spearheading this technological change.

Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring

Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.

Analysis of snoring to determine the site of obstruction in obstructive sleep apnea syndrome

BACKGROUND

The aim of this study was to integrate the physical findings of drug-induced sleep endoscopy with snoring sound analysis in patients with obstructive sleep apnea/hypopnea syndrome (OSAS) and to compare the findings with previously published data.

METHODS

This was a prospective, non-randomized study. Participants were all candidates for surgical treatment of OSAS and formed three groups, retropalatal (RP) obstructions, retrolingual (RL) obstructions, and multilevel (ML) obstructions. At the time of DISE, recordings of concurrent snoring sounds were made. Mean pitch frequency, peak sound frequency, and fundamental frequency (Fo) components were determined.

RESULTS

A total of 55 participants had mean age 46.2 ± 7.3 years, mean BMI 30.0 ± 3.7 kg/m², and included 11 women (20%). Differences in mean pitch frequency, Fo, and peak sound frequency were all statistically significant between the RP and RL (p = 0.001), between ML and RL (p = 0.025) but were not significantly different between RP and ML. Mean pitch frequency of RP was lower than RL, and ML frequency was between RL and RP. The sound analysis graphics revealed RP waves with sharp peaks and lower frequencies and RL with smooth curves and higher frequencies. ML showed irregular patterns. Mean pitch frequency of RL was always above 400 Hz, whereas RP was below this value.

CONCLUSIONS

It is feasible to apply sound analysis to determine the site of obstruction during DISE. Combining the data may help surgeons make more accurate assessments of the pattern of the disease.

Acoustic analyses of snoring sounds using a smartphone in patients undergoing septoplasty and turbinoplasty

PURPOSE

Several studies have been performed using recently developed smartphone-based acoustic analysis techniques. We investigated the effects of septoplasty and turbinoplasty in patients with nasal septal deviation and turbinate hypertrophy accompanied by snoring by recording the sounds of snoring using a smartphone and performing acoustic analysis.

METHODS

A total of 15 male patients who underwent septoplasty with turbinoplasty for snoring and nasal obstruction were included in this prospective study. Preoperatively and 2 months after surgery, their bed partners or caregivers were instructed to record the snoring sounds. The intensity (dB), formant frequencies (F₁, F₂, F₃, and F₄), spectrogram pattern, and visual analog scale (VAS) score were analyzed for each subject.

RESULTS

Overall snoring sounds improved after surgery in 12/15 (80%) patients, and there was significant improvement in the intensity of snoring sounds after surgery (from 64.17 ± 12.18 dB to 55.62 ± 9.11 dB, p = 0.018). There was a significant difference in the F₁ formant frequency before and after surgery (p = 0.031), but there were no significant differences in F₂, F₃, or F₄. The change in F₁ indicated that patients changed from mouth breathing to normal breathing. The degree of subjective snoring sounds improved significantly after surgery (VAS: from 5.40 ± 1.55 to 3.80 ± 1.26, p = 0.003).

CONCLUSION

Our results confirm that snoring is reduced when nasal congestion is improved, and they demonstrate that smartphone-based acoustic analysis of snoring sounds can be useful for diagnosis.

Characterisation of Upper Airway Collapse in OSA Patients Using Snore Signals: A Cluster Analysis Approach

This paper provides the results of an unsupervised learning algorithm that characterize upper airway collapse in obstructive sleep apnoea (OSA) patients using snore signal during hypopnoea events. Knowledge regarding the site-of-collapse could improve the ability in choosing the most appropriate treatment for OSA and thereby improving the treatment outcome. In this study, we implemented an unsupervised k-means clustering algorithm to label the snore data during hypopnoea events. Audio data during sleep were recorded simultaneously with full-night polysomnography with a ceiling microphone. Various time and frequency features of audio signal during hypopnoea were extracted. A systematic evaluation method was implemented to find the optimal feature set and the optimal number of clusters using silhouette coefficients. Using these optimal feature sets, we clustered the snore data into two. Performance of the proposed model showed that the data fit well in two clusters with a mean silhouette coefficients of 0.79. Also, the clusters achieved an overall accuracy of 62% for predicting tongue/non-tongue related collapse.

Breath variability increases in the minutes preceding obstructive sleep apneic events

PURPOSE

It is unclear if there is a consistent signature in breath patterns prior to an impending obstructive apneic event in patients with sleep-disordered breathing (SDB).

OBJECTIVE

To use continuous recordings of ambient sound in sleep using a smartphone to track auditory signatures of breaths and measure their regularity preceding apneic events.

METHODS

We studied 50 patients evaluated for SDB in whom sound was recorded using smartphones concurrently with polysomnography (PSG). Whole-night sound files were analyzed for time and frequency domain analyses of breath periodicity during periods of normal and sleep-disordered breathing.

RESULTS

Fifty patients (44% women, 42.0 ± 9.4 years old, BMI 32.8 ± 10.8 kg/m²) recorded sound, of whom 30 were diagnosed with OSA and 20 were not. We analyzed a total of 497 apneic (≥10 s) and 481 non-apneic intervals, confirmed by PSG. Interbreath intervals were 3.75 ± 0.62 s for 1 min in quiet breathing, with SD 1.11 ± 0.48 s that increased to 4.16 ± 3.06 s in successive 60-s epochs up to apnea (p < 0.001). Interbreath SD in the 60 s immediately preceding apnea was higher than the SD in random non-apneic periods (p < 0.01, ANOVA). Interbreath SD ≥1.49 s gave 87.3% sensitivity and 86.5% specificity for predicting apnea in the next minute (c-statistic 0.94).

CONCLUSIONS

Breaths increase in variability minutes before proven obstructive apnea in patients with suspected SDB. These results suggest that it may be possible to predict and thus potentially avert apneic events and provide insights into events leading to SDB.

TRIAL REGISTRATION

NCT03288376, clinicaltrials.org.

A New Feature with the Potential to Detect the Severity of Obstructive Sleep Apnoea via Snoring Sound Analysis

The severity of obstructive sleep apnoea (OSA) is diagnosed with polysomnography (PSG), during which patients are monitored by over 20 physiological sensors overnight. These sensors often bother patients and may affect patients’ sleep and OSA. This study aimed to investigate a method for analyzing patient snore sounds to detect the severity of OSA. Using a microphone placed at the patient’s bedside, the snoring and breathing sounds of 22 participants were recorded while they simultaneously underwent PSG. We examined some features from the snoring and breathing sounds and examined the correlation between these features and the snore-specific apnoea-hypopnea index (ssAHI), defined as the number of apnoea and hypopnea events during the hour before a snore episode. Statistical analyses revealed that the ssAHI was positively correlated with the Mel frequency cepstral coefficients (MFCC) and volume information (VI). Based on clustering results, mild snore sound episodes and snore sound episodes from mild OSA patients were mainly classified into cluster 1. The results of clustering severe snore sound episodes and snore sound episodes from severe OSA patients were mainly classified into cluster 2. The features of snoring sounds that we identified have the potential to detect the severity of OSA.

Inter-rater reliability between experienced and inexperienced otolaryngologists using Koo's drug-induced sleep endoscopy classification system

PURPOSE

An ideal, drug-induced sleep endoscopy (DISE) classification system should cover all the upper airways, be simple and practical, and quantify the severity of any obstruction. Excellent validity and reliability are essential. We explored the inter-rater reliability of Koo's DISE classification system in the hands of experienced and inexperienced otolaryngologists.

METHODS

We retrospectively analyzed video images of 100 patients who underwent DISE examination in our hospital between 2015 and 2018. Three experienced and three inexperienced otolaryngologists reviewed and scored all images. We calculated the inter-rater reliabilities of the two groups of otolaryngologists.

RESULTS

Independent of the extent of experience with DISE, detection of retropalatal obstructions (overall agreement: 0.87; kappa value: 0.60), and the degree of such obstructions (overall agreement: 0.67; kappa value: 0.52) were more consistent than were the detection of retrolingual obstructions (overall agreement: 0.61, kappa value: 0.37) and the degree of retrolingual obstructions (overall agreement: 0.20, kappa value: 0.35). Inexperienced observers were in good agreement for palatal obstructions and experienced observers were in good agreement for tongue-base obstructions. All of the otolaryngologists found it difficult to detect a lateral pharyngeal wall obstruction at the retrolingual level.

CONCLUSION

Koo's DISE classification system focuses on surgical treatment, especially by otolaryngologists, and the degree of agreement between the experienced and inexperienced observers was relatively high. The participants' level of experience had a strong impact on scoring. The less-experienced otolaryngologists tended to overlook tongue-base obstructions, focusing instead on relatively simple retropalatal obstructions. In the future, development of a DISE classification system that can be accepted globally will be necessary.

A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients

In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, it has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. The proposed app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smartphone. Furthermore, the algorithm for crackle detection was based on a time-varying autoregressive modeling. The performance of the automated detector was analyzed using: (1) synthetic fine and coarse crackle sounds randomly inserted to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios, and (2) real bedside acquired respiratory sounds from patients with interstitial diffuse pneumonia. In simulated scenarios, for fine crackles, an accuracy ranging from 84.86% to 89.16%, a sensitivity ranging from 93.45% to 97.65%, and a specificity ranging from 99.82% to 99.84% were found. The detection of coarse crackles was found to be a more challenging task in the simulated scenarios. In the case of real data, the results show the feasibility of using the developed mobile health system in clinical no controlled environment to help the expert in evaluating the pulmonary state of a subject.

Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

OBJECTIVES

To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratory sounds recorded during polysomnography during all sleep stages between sleep onset and offset.

METHODS

Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audio recordings were performed with an air-conduction microphone during polysomnography. Analyses included all sleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmented into 5-s windows and sound features were extracted. Prediction models were established and validated with 10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for three different threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, including accuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under the curve (AUC) of the receiver operating characteristic were computed.

RESULTS

A total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2 , and 23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughout sleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Prediction performances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%, 81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were 89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30.

CONCLUSION

This study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificity of >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithms based on respiratory sounds may have a high value for prescreening OSA with mobile devices.

Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography

PURPOSE

Diagnosis of obstructive sleep apnea by the gold-standard of polysomnography (PSG), or by home sleep testing (HST), requires numerous physical connections to the patient which may restrict use of these tools for early screening. We hypothesized that normal and disturbed breathing may be detected by a consumer smartphone without physical connections to the patient using novel algorithms to analyze ambient sound.

METHODS

We studied 91 patients undergoing clinically indicated PSG. Phase I: In a derivation cohort (n = 32), we placed an unmodified Samsung Galaxy S5 without external microphone near the bed to record ambient sounds. We analyzed 12,352 discrete breath/non-breath sounds (386/patient), from which we developed algorithms to remove noise, and detect breaths as envelopes of spectral peaks. Phase II: In a distinct validation cohort (n = 59), we tested the ability of acoustic algorithms to detect AHI < 15 vs AHI > 15 on PSG.

RESULTS

Smartphone-recorded sound analyses detected the presence, absence, and types of breath sound. Phase I: In the derivation cohort, spectral analysis identified breaths and apneas with a c-statistic of 0.91, and loud obstruction sounds with c-statistic of 0.95 on receiver operating characteristic analyses, relative to adjudicated events. Phase II: In the validation cohort, automated acoustic analysis provided a c-statistic of 0.87 compared to whole-night PSG.

CONCLUSIONS

Ambient sounds recorded from a smartphone during sleep can identify apnea and abnormal breathing verified on PSG. Future studies should determine if this approach may facilitate early screening of SDB to identify at-risk patients for definitive diagnosis and therapy.

CLINICAL TRIALS

NCT03288376; clinicaltrials.org.

Acoustic analysis of snoring sounds originating from different sources determined by drug-induced sleep endoscopy

OBJECTIVE

To discuss the possibility of fundamental frequency (F0) and formant frequency (FF) to generally differentiate the sources of snoring sounds determined by drug-induced sleep endoscopy (DISE).

METHODS

A total of 74 snoring subjects underwent DISE and snoring sounds were recorded simultaneously. The noise-suppressed snoring sounds were analyzed and classified into different groups based on the sources of vibration identified by DISE. F0 and FFs were calculated.

RESULTS

Totally, 516 snoring sounds from three vibrating sources (the palate, combined the palate and the lateral wall, the lateral wall) of 47 patients were divided into three groups then analyzed. The levels of F0 and FFs for each group follow the order: Group 1 < Group 2 < Group 3. There was statistical difference between Group 1 and other groups in F0 and F2 (p < .05). The area under the receiver-operator curves (AUC) was F0, at 0.727, and the cut-off value was 134.2 Hz; and F2, at 0.654, and the cut-off value was 2028.0 Hz.

CONCLUSIONS

F0 and the second formant frequency (F2) are found to be significantly lower in palatal snoring sound. F0 might be a significant in distinguishing palatal snoring sound from non-palatal snoring sound. F2 is more significant than F1 and F3 in identifying the sources of the snoring sounds but is less sensitive than F0.