Acoustic analysis of overnight consecutive snoring sounds by sound pressure levels

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.

Using entropy of snoring, respiratory effort and electrocardiography signals during sleep for OSA detection and severity classification

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is a very prevalent disease and its diagnosis is based on polysomnography (PSG). We investigated whether snoring-sound-, very low frequency electrocardiogram (ECG-VLF)- and thoraco-abdominal effort- PSG signal entropy values could be used as surrogate markers for detection of OSA and OSA severity classification.

METHODS

The raw data of the snoring-, ECG- and abdominal and thoracic excursion signal recordings of two consecutive full-night PSGs of 86 consecutive patients (22 female, 53.74 ± 12.4 years) were analyzed retrospectively. Four epochs (30 s each, manually scored according to the American Academy of Sleep Medicine standard) of each sleep stage (N1, N2, N3, REM, awake) were used as the ground truth. Sampling entropy (SampEn) of all the above signals was calculated and group comparisons between the OSA severity groups were performed. In total, (86x4x5 = )1720 epochs/group/night were included in the training set as an input for a support vector machine (SVM) algorithm to classify the OSA severity classes. Analyses were performed for first- and second-night PSG recordings separately.

RESULTS

Twenty-seven patients had mild (RDI = ≥ 5/h but <15/h), 21 patients moderate (RDI ≥15/h but <30/h) and 23 patients severe OSA (RDI ≥30/h). Fifteen patients had an RDI <5/h and were therefore considered non-OSA. Using SE on the above three PSG signal data and using a SVM pipeline, it was possible to distinguish between the four OSA severity classes. The best metric was snoring signal-SE. The area-under-the-curve (AUC) calculations showed reproducible significant results for both nights of PSG. The second night data were even more significant, with non-OSA (R) vs. light OSA (L) 0.61, R vs. moderate (M) 0.68, R vs. heavy OSA (H) 0.84, L vs. M 0.63, M vs. H 0.65 and L vs. H 0.82. The results were not confounded by age or gender.

CONCLUSIONS

SampEn of either snoring-, very low ECG-frequencies- or thoraco-abdominal effort signals alone may be used as a surrogate marker to diagnose OSA and even predict OSA severity. More specifically, in this exploratory study snoring signal SampEn showed the greatest predictive accuracy for OSA among the three signals. Second night data showed even more accurate results for all three parameters than first-night recordings. Therefore, technologies using only parts of the PSG signal, e.g. sound-recording devices, may be used for OSA screening and OSA severity group classification.

Effects of demographic and sleep-related factors on snoring sound parameters

OBJECTIVE

To investigate the effect of frequently reported between-individual (viz., age, gender, body mass index [BMI], and apnea-hypopnea index [AHI]) and within-individual (viz., sleep stage and sleep position) snoring sound-related factors on snoring sound parameters in temporal, intensity, and frequency domains.

METHODS

This study included 83 adult snorers (mean ± SD age: 42.2 ± 11.3 yrs; male gender: 59%) who underwent an overnight polysomnography (PSG) and simultaneous sound recording, from which a total of 131,745 snoring events were extracted and analyzed. Data on both between-individual and within-individual factors were extracted from the participants' PSG reports.

RESULTS

Gender did not have any significant effect on snoring sound parameters. The fundamental frequency (FF; coefficient = -0.31; P = 0.02) and dominant frequency (DF; coefficient = -12.43; P < 0.01) of snoring sounds decreased with the increase of age, and the second formant increased (coefficient = 22.91; P = 0.02) with the increase of BMI. Severe obstructive sleep apnea (OSA; AHI ≥30 events/hour), non-rapid eye movement sleep stage 3 (N3), and supine position were all associated with more, longer, and louder snoring events (P < 0.05). Supine position was associated with higher FF and DF, and lateral decubitus positions were associated with higher formants.

CONCLUSIONS

Within the limitations of the current patient profile and included factors, AHI was found to have greater effects on snoring sound parameters than the other between-individual factors. The included within-individual factors were found to have greater effects on snoring sound parameters than the between-individual factors under study.

Long-term average spectrum measures of consecutive snore sounds from different sources determined by drug-induced sleep endoscopy

STUDY OBJECTIVES

The goal of this study was to investigate the value of the long-term average spectrum in the acoustic analysis of snore sounds arising from different sources in the upper airway.

METHODS

Long-term average spectrum was used to analyze sequences of 10 consecutive snore sounds that had been divided into 2 groups, soft-palate type and lateral-wall type, according to the vibration site generating the snore sounds and the patterns of soft tissue collapse in the upper airway as identified by drug-induced sleep endoscopy. We calculated the first spectral peak, mean spectral energy, high-frequency energy, 0-1 kHz spectral energy, 1-5 kHz spectral energy, and 0-1 kHz/1-5 kHz difference from each group and compared the differences between them.

RESULTS

All parameters except mean spectral energy showed significant differences between the 2 groups. The first spectral peak of less than 265.53 Hz, and the 0-1k/1-5 kHz difference of less than -11.6 dB strongly suggests soft-palate-type snore sounds.

CONCLUSIONS

Long-term average spectrum has potential application for snore sound source identification. We recommend using first spectral peak and a 0-1 kHz/1-5 Hz difference to identify soft-palate-type snore sounds.

CITATION

Peng H, Xu H, Xu Z, Jia R, Yu H. Long-term average spectrum measures of consecutive snore sounds from different sources determined by drug-induced sleep endoscopy. J Clin Sleep Med. 2023;19(1):145-150.

Correlation between snoring sounds and obstructive sleep apnea in adults: a meta-regression analysis

OBJECTIVE

Snoring is a dominant clinical symptom in patients with obstructive sleep apnea (OSA), and analyzing snoring sounds might be a potential alternative to polysomnography (PSG) for the assessment of OSA. This study aimed to systematically examine the correlation between the snoring sounds and the apnea-hypopnea index (AHI) as the measures of OSA severity.

MATERIAL AND METHODS

A comprehensive literature review using the MEDLINE, Embase, Cochrane Library, Scopus, and PubMed databases identified the published studies reporting the correlations between and severity of snoring and the AHI values by meta-regression analysis.

RESULTS

In total, 13 studies involving 3,153 adult patients were included in this study. The pooled correlation coefficient for snoring sounds and AHI values was 0.71 (95%CI: 0.49, 0.85) from the random-effects meta-analysis with the Knapp and Hartung adjustment. The I 2 and chi-square Q test demonstrated significant heterogeneity (97.6% and p<0.001). After adjusting for the effects of the other covariates, the mean value of the Fisher's r-to-z transformed correlation coefficient would have 0.80 less by the snoring rate (95%CI = -1.02, -0.57), 1.46 less by the snoring index (95%CI = -1.85, -1.07), and 0.21 less in the mean body mass index (95%CI = -0.31, -0.11), but 0.15 more in the mean age (95%CI = 0.10, 0.20). It fitted the data very well (R 2=0.9641).

CONCLUSION

A high correlation between the severity of snoring and the AHI was found in the studies with PSG. As compared to the snoring rate and the snoring index, the snoring intensity, the snoring frequency, and the snoring time interval index were more sensitive measures for the severity of snoring.

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.

Systematic review of the different aspects of primary snoring

Primary snoring, also known as simple or non-apnoeic snoring, is regarded as the first stage of sleep disordered breathing without severe medical consequences for the snorer and co-sleeper. Although it is a highly prevalent phenomenon in the general population, our knowledge is limited because of the lack of a consensus on terminology. This systematic review of the aspects used in the definitions of simple/primary snoring was conducted to obtain an inventory of current practices and compare these definitions with the conceptual definition of the American Academy of Sleep Medicine. PubMed and Web of Science were searched from July 2016 onwards without any language limitations, and 362 references were obtained. After selection based on titles, 39 remained, among which 29 contained a definition or reference to a definition. In 69% of the studies, a cut-off <5 apnoea/Hypopnoea events per hour of sleep on the Apnoea-Hypopnoea Index was used. Despite this tendency, the cut-offs ranged from 0 to <15/h. Unfortunately, the cut-off and occasional requirements did not match the conceptual definition of the American Academy of Sleep Medicine. A consensus must be reached on an operational and clinically relevant definition based on the clear conceptual definition.

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.

Breathing and Snoring Sound Characteristics during Sleep in Adults

STUDY OBJECTIVES

Sound level meter is the gold standard approach for snoring evaluation. Using this approach, it was established that snoring intensity (in dB) is higher for men and is associated with increased apnea-hypopnea index (AHI). In this study, we performed a systematic analysis of breathing and snoring sound characteristics using an algorithm designed to detect and analyze breathing and snoring sounds. The effect of sex, sleep stages, and AHI on snoring characteristics was explored.

METHODS

We consecutively recruited 121 subjects referred for diagnosis of obstructive sleep apnea. A whole night audio signal was recorded using noncontact ambient microphone during polysomnography. A large number (> 290,000) of breathing and snoring (> 50 dB) events were analyzed. Breathing sound events were detected using a signal-processing algorithm that discriminates between breathing and nonbreathing (noise events) sounds.

RESULTS

Snoring index (events/h, SI) was 23% higher for men (p = 0.04), and in both sexes SI gradually declined by 50% across sleep time (p < 0.01) independent of AHI. SI was higher in slow wave sleep (p < 0.03) compared to S2 and rapid eye movement sleep; men have higher SI in all sleep stages than women (p < 0.05). Snoring intensity was similar in both genders in all sleep stages and independent of AHI. For both sexes, no correlation was found between AHI and snoring intensity (r = 0.1, p = 0.291).

CONCLUSIONS

This audio analysis approach enables systematic detection and analysis of breathing and snoring sounds from a full night recording. Snoring intensity is similar in both sexes and was not affected by AHI.

Predicting Obstructive Sleep Apnea with Periodic Snoring Sound Recorded at Home

STUDY OBJECTIVES

The cost-effectiveness of diagnosing obstructive sleep apnea (OSA) could be improved by using a preliminary screening method among subjects with no suspicion of other sleep disorders. We aimed to evaluate the diagnostic value of periodic snoring sound recorded at home.

METHODS

We included 211 subjects, aged 18-83 (130 men), who were referred to our laboratory for suspicion of OSA, and had a technically successful overnight polygraphy, measured with the Nox T3 Sleep Monitor (Nox Medical, Iceland) with a built-in microphone. We analyzed the percentage of periodic snoring during the home sleep apnea study.

RESULTS

Apnea-hypopnea index (AHI) ranged from 0.1 to 116 events/h and the percentage of periodic snoring from 1% to 97%. We found a strong positive correlation (r = 0.727, p < 0.001) between periodic snoring and AHI. The correlation was slightly stronger among female, younger, and obese subjects. The best threshold value of the periodic snoring for predicting an AHI > 15 events/h with as high sensitivity as possible was found to be 15%. There, sensitivity was 93.3%, specificity 35.1%, and negative predictive value 75.0%.

CONCLUSIONS

According to our results, it is possible to set a periodic snoring threshold (15% or more) for the subject to advance to further sleep studies. Together with medical history and prior to more expensive studies, measuring periodic snoring at home is a simple and useful method for predicting the probability of OSA, in particular among women who are often unaware of their apnea-related snoring.