Vibration signals of snoring as a simple severity predictor for obstructive sleep apnea

Wu, Hsien‐Tsai; Pan, Wen-Yao; Liu, An-Bang; Su, Mu-Chun; Chen, Hong-Ruei; Tsai, I‐Ting; Lin, Meng-Chih; Sun, Cheuk‐Kwan

doi:10.1111/crj.12237

Cited by 6 publications

(9 citation statements)

References 35 publications

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“…In studies that used sound intensity as a marker for snoring, stronger relationships between SI and AHI (with correlation coefficients between 0.73 29 and 0.91 14 ) were observed than between SI and AHI in the current study. However, in one of those studies, only patients with OSA were included; thus, snoring was not examined in patients without OSA.…”

Section: Discussioncontrasting

confidence: 57%

“…29 In the other study, snoring was defined as amplitude bursts preceded by an apnea or a hypopnea, so that snoring was associated with apneas and hypopneas by design, which would artificially enhance any relationship between snoring and AHI. 14 Accordingly, those studies are not comparable to ours, and would overestimate the relationship between snoring and OSA severity, and underestimate the degree of snoring that is not associated with OSA. Our approach, conversely, detects snoring regardless of the presence or absence of apneas and hypopneas, and therefore should provide a more accurate assessment of snoring frequency in patients with and without OSA.…”

Section: Discussioncontrasting

confidence: 57%

“…12 Few studies tried to objectively investigate the relationship between snoring and OSA severity, but such studies have relied mainly on the sound intensity (amplitude) or sound bursts. 13,14 This approach assumes that snoring has a higher sound intensity than nonsnoring breath sounds. This approach has limitations; first, snoring is not uniform in intensity.…”

Section: Introductionmentioning

confidence: 99%

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

Alshaer

Hummel

Mendelson

et al. 2019

Journal of Clinical Sleep Medicine

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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.

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Section: Discussioncontrasting

confidence: 57%

Section: Discussioncontrasting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Alshaer

Hummel

Mendelson

et al. 2019

Journal of Clinical Sleep Medicine

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“…The corresponding researchers attempted to identify snoring sounds to estimate the snoring rate and then compared the estimated snoring rate to the AHI data obtained from PSG or other forms of sonography. The second subgroup contained studies that used a snoring index (snore number/sleep hour); 4 studies reported the correlations between AHI values and snoring index [17][18][19][20] . Snoring signals were estimated by recording the number of snores, which were detected by using either PSG or a special device at home.…”

Section: Resultsmentioning

confidence: 99%

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

Chiang¹,

Lin²,

Lu³

et al. 2022

Sleep Sci

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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.

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“…Snoring is the most common symptom of OSA [24]; however, up to 45% of the general population are habitual snorers and most do not have OSA [25]. A few studies have used sound amplitude to identify snoring and found a positive relationship between snoring and OSA severity [26,27]. A recent study used a more sophisticated snoring detection method to analyse the relationship between the number of snores per hour of sleep and apnoea-hypopnoea index, and found that the presence and severity of snoring have only a weak positive relationship to the presence and severity of OSA [28].…”

Section: Resultsmentioning

confidence: 99%

The B‐APNEIC score: distilling the STOP‐Bang questionnaire to identify patients at high risk for severe obstructive sleep apnoea

et al. 2021

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Summary The STOP‐Bang questionnaire is an established clinical screening tool to identify the risk of having mild, moderate or severe obstructive sleep apnoea using eight variables. It is unclear whether all eight variables contribute equally to the risk of clinically significant obstructive sleep apnoea. We analysed each variable for its contribution to detecting obstructive sleep apnoea; based on the results, we investigated whether the STOP‐Bang questionnaire could be abbreviated to identify patients at high risk for severe obstructive sleep apnoea. We recruited patients with suspected obstructive sleep apnoea who were referred for overnight polysomnography. We used multivariable logistic regression to investigate the association of STOP‐Bang parameters with severe obstructive sleep apnoea based on clinical and polysomnography data. Regression estimates were used to select variables to create the novel B‐APNEIC score. We constructed receiver operating characteristic curves for the STOP‐Bang questionnaire and B‐APNEIC scores to identify patients with severe obstructive sleep apnoea and compared the areas under the curve using the DeLong method. Of the 275 patients enrolled, 32% (n = 88) had severe obstructive sleep apnoea. Logistic regression demonstrated that neck circumference (OR 2.20; 95%CI 1.10–4.40, p = 0.03) was the only variable independently associated with severe obstructive sleep apnoea. Observed apnoea during sleep, blood pressure and body mass index were the three next most closely trending predictors of severe obstructive sleep apnoea and were included along with neck circumference in the B‐APNEIC score. Receiver operating curves demonstrated that the areas under the curve for STOP‐Bang vs. B‐APNEIC were comparable for identifying patients with severe obstructive sleep apnoea (OR 0.75; 95%CI 0.68‐0.81 vs. OR 0.75; 95%CI 0.68–0.81: p = 0.99, respectively). Our results suggest that the B‐APNEIC score is a simplified adaptation of the STOP‐Bang questionnaire with equivalent effectiveness in identifying patients with severe obstructive sleep apnoea. Further studies are needed to validate and build on our findings.

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Vibration signals of snoring as a simple severity predictor for obstructive sleep apnea

Abstract: SBI may serve as a portable tool for screening patients and assessing OSA severity in a non-hospital setting.

Cited by 6 publications

References 35 publications

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

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

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

The B‐APNEIC score: distilling the STOP‐Bang questionnaire to identify patients at high risk for severe obstructive sleep apnoea

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