Evaluation of acoustic characteristics of snoring sounds obtained during drug-induced sleep endoscopy

Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3-7% of males and 2-5% of females. In the United States alone, 50-70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis.

Tracheal Sound Analysis

Tracheal sound sensors provide multiple respiratory signals that are valuable for studying upper airway characteristics. This chapter reviews the original work and ongoing research on tracheal sound analysis in relation to upper airway obstruction during sleep. Past and current research suggest that being associated with other sleep study recording sensors and advanced signal processing techniques, tracheal sound analysis can extensively contribute to the diagnosis and assessment of sleep-disordered breathing.

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.

Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.

Objective Relationship Between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning

STUDY OBJECTIVES

Snoring is perceived to be directly proportional to sleep apnea severity, especially obstructive sleep apnea (OSA), but this notion has not been thoroughly and objectively evaluated, despite its popularity in clinical practice. This might lead to overdiagnosis or underdiagnosis of OSA. The goal of this study is to examine this notion and objectively quantify the relationship between sleep apnea and snoring detected using advanced signal processing algorithms.

METHODS

We studied adults referred for polysomnography, from which the apnea-hypopnea index (AHI) was derived. Breath sounds were recorded simultaneously, from which snoring was accurately quantified using acoustic analysis of breath sounds and machine-learning computer algorithms. The snore index (SI) was calculated as the number of snores per hour of sleep.

RESULTS

In 235 patients, the mean AHI was 20.2 ± 18.8 and mean SI was 320.2 ± 266.7 events/h. On the one hand, the overall correlation between SI and AHI was weak but significant (r = .32, P < .0001). There was a significant stepwise increase in SI with increasing OSA severity, but with a remarkable overlap in SI among OSA severity categories. On the other hand, SI had weak negative correlation with central AHI (r = -.14, P = .035). SI had modest positive and negative predictive values for OSA (0.63 and 0.62 on average, respectively) and good sensitivity but low specificity (0.91 and 0.31 on average, respectively) attributed to the large number of snorers without OSA.

CONCLUSIONS

Snoring on its own is probably of limited usefulness in assessing sleep apnea presence and severity, because of its weak relationship with AHI. Thus, the complaint of snoring should be interpreted with caution to avoid unnecessary referrals for sleep apnea testing. Conversely, clinicians should be aware of the possibility of missing diagnosis of patients with sleep apnea who have minimal snoring.

Drug-induced sleep endoscopy: from obscure technique to diagnostic tool for assessment of obstructive sleep apnea for surgical interventions

PURPOSE OF REVIEW

Provide a practical update on drug-induced sleep endoscopy (DISE) for anesthesia providers, which can also serve as a reference for those preparing to establish a DISE program.

RECENT FINDINGS

New developments in surgical approaches to OSA and the growing global incidence of the condition have stimulated increased interest and demand for drug-induced sleep endoscopy. New techniques include transoral robotic surgery and hypoglossal nerve stimulation. Recent DISE literature has sought to address numerous debates including relevance of DISE findings to those during physiologic sleep and the most appropriate depth and type of sedation for DISE. Propofol and dexmedetomidine have supplanted midazolam as the drugs of choice for DISE. Techniques based on pharmacokinetic models of propofol are superior to empiric dosing with regard to risk of respiratory compromise and the reliability of dexmedetomidine to achieve adequate conditions for a complete DISE exam is questionable.

SUMMARY

The role of DISE in surgical evaluation and planning for treatment of OSA continues to develop. Numerous questions as to the optimal anesthetic approach remain unanswered. Multicenter studies that employ a standardized approach using EEG assessment, pharmacokinetic-pharmacodynamic modelling, and objectively defined clinical endpoints will be helpful. There may be benefit to undertaking DISE studies in non-OSA patients.

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.

Snoring Sounds Predict Obstruction Sites and Surgical Response in Patients with Obstructive Sleep Apnea Hypopnea Syndrome

Snoring sounds generated by different vibrators of the upper airway may be useful indicators of obstruction sites in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). This study aimed to investigate associations between snoring sounds, obstruction sites, and surgical responses (≥50% reduction in the apnea-hypopnea index [AHI] and <10 events/hour) in patients with OSAHS. This prospective cohort study recruited 36 OSAHS patients for 6-hour snoring sound recordings during in-lab full-night polysomnography, drug-induced sleep endoscopy (DISE), and relocation pharyngoplasty. All patients received follow-up polysomnography after 6 months. Fifteen (42%) patients with at least two complete obstruction sites defined by DISE were significantly, positively associated with maximal snoring sound intensity (40-300 Hz; odds ratio [OR], 1.25, 95% confidence interval [CI] 1.05-1.49) and body mass index (OR, 1.48, 95% CI 1.02-2.15) after logistic regression analysis. Tonsil obstruction was significantly, inversely correlated with mean snoring sound intensity (301-850 Hz; OR, 0.84, 95% CI 0.74-0.96). Moreover, baseline tonsil obstruction detected by either DISE or mean snoring sound intensity (301-850 Hz), and AHI could significantly predict the surgical response. Our findings suggest that snoring sound detection may be helpful in determining obstruction sites and predict surgical responses.