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

Herzog, Michael; Plößl, Sebastian; Glien, Alexander; Herzog, Beatrice; Rohrmeier, Christian; Kühnel, Thomas; Plontke, Stefan K.; Kellner, Patrick

doi:10.1007/s11325-014-1085-7

Cited by 15 publications

(8 citation statements)

References 34 publications

(42 reference statements)

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“…Future works can be done mainly in the following three areas: First, more potentially useful acoustic features can be tested, specifically, psychoacoustic characteristics (e.g., loudness, sharpness, roughness, and fluctuation strength), and higher order statistical model-based features (e.g., bispectrum), which have been studied in [56] and [57]. Some fundamental work to explore the relationship between feature properties and the anatomical changes in the upper airway can help to better understand the SnS generation mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Classification of the Excitation Location of Snore Sounds in the Upper Airway by Acoustic Multifeature Analysis

Qian

Janott

Pandit

et al. 2017

IEEE Trans. Biomed. Eng.

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Section: Discussionmentioning

confidence: 99%

Classification of the Excitation Location of Snore Sounds in the Upper Airway by Acoustic Multifeature Analysis

Qian

Janott

Pandit

et al. 2017

IEEE Trans. Biomed. Eng.

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“…This is comparable with the finding that the mean peak frequencies from 800 Hz to 2000 Hz of the first snoring sounds after lower level obstructive apnea are higher than those after upper level obstructive apnea 19 . Using psychoacoustic algorithms, velar snoring has been shown to be rougher (rapid amplitude modulation, 15–300 Hz) than tonsillar snoring during DISE 29 , and also that post-apnoeic snoring has the largest fluctuation (slower amplitude modulation, <20 Hz). In this study, we confirmed that multi-level obstructions caused apnea during DISE, however we found that they can be predicted by B1-Imax in natural sleep without detecting the strength of fluctuation.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we confirmed that multi-level obstructions caused apnea during DISE, however we found that they can be predicted by B1-Imax in natural sleep without detecting the strength of fluctuation. Although tonsillar snoring has the highest sharpness (high-frequency signal) 29 , tonsil obstruction is inversely related to B2-Imean. Moreover, we also find that tonsil size was significantly associated with total-SI ( r = −0.34, p = 0.041), B1-SI ( r = −0.44, p = 0.007), and total-Fmean ( r = 0.36, p = 0.031) and statistically insignificantly related to partial-to-complete tonsil obstruction ( r = 0.25, p = 0.15).…”

Section: Discussionmentioning

confidence: 99%

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

Lee

et al. 2016

Sci Rep

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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.

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“…For example, snoring associated with obstructive events is louder than snoring not associated with obstructive events. 15 Second, this approach does not take into account that some nonsnoring breath sounds, such as postapneic hyperventilation or gasping, which have high amplitude, might be falsely misclassified as snoring by an amplitude-based snoring detection system. Accordingly, using a sound amplitude threshold to detect snoring can result in missing low amplitude snores or falsely labeling high amplitude breath sounds as snoring.…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 15 publications

References 34 publications

Classification of the Excitation Location of Snore Sounds in the Upper Airway by Acoustic Multifeature Analysis

Classification of the Excitation Location of Snore Sounds in the Upper Airway by Acoustic Multifeature Analysis

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

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

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