Snoring: analysis, measurement, clinical implications and applications

Dalmasso, F; Prota, Roberto

doi:10.1183/09031936.96.09010146

Cited by 153 publications

(115 citation statements)

References 49 publications

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“…14,15,28 An important problem in dealing with objective respiratory sounds is the comparison of the data of various investigators and correct interpretation. 50 This study shows that a snore detection system can provide an objective quantitative measure for whole-night snore patterns. Further studies are needed both to reinforce our findings by recruiting subjects from primary care clinics and by validating this screening tool in an at-home environment.…”

Section: Discussionmentioning

confidence: 99%

Breathing and Snoring Sound Characteristics during Sleep in Adults

Levartovsky

Dafna

Zigel

et al. 2016

Journal of Clinical Sleep Medicine

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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.

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Section: Discussionmentioning

confidence: 99%

Breathing and Snoring Sound Characteristics during Sleep in Adults

Levartovsky

Dafna

Zigel

et al. 2016

Journal of Clinical Sleep Medicine

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“…Moreover, in [1], M. Cavusoglu proposed another technique to detect snoring by using principle component analysis (PCA) as a feature extraction. The power spectrum of the data set was necessarily calculated to obtain the sub-band energy distribution before applying PCA.…”

Section: Snoring Detectionmentioning

confidence: 99%

“…About 50% of the adult population snores frequently. Snoring has been also reported as a risk factor for developing disorders such as ischemic brain infraction, systemic arterial hyper-arterial hypertension, coronary artery disease, and sleep disturbance [1]. Several studies have shown the relationship between snoring and OSAS, which is usually associated with loud and heavy snoring [1].…”

Section: Introductionmentioning

confidence: 99%

“…Researches ranging from laboratory-based studies to real environment studies have produced higher detection rates using several classification methods. These methods include robust linear regression with principal component analysis (PCA) on the sub-band energy distributions [1], AdaBoost classifier using acoustic features from time and spectral domains [3], hidden Markov model (HMM) [4], fuzzy mean clustering [5], wavelet-based spectral analysis [6], genetic algorithm, and a support vector machine (SVM) [7]. Most of these classification methods use information extracted from acoustic signals during sleep.…”

Section: Introductionmentioning

confidence: 99%

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Sleep snoring detection using multi-layer neural networks

Nguyen

Won

2015

BME

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Abstract. Snoring detection is important for diagnosing obstructive sleep apnea syndrome (OSAS) and other respiratory sleep disorders. In general, audio signal processing such as snoring sound analysis uses the frequency characteristics of the signal. Recently, a correlational filter Multilayer Perceptron neural network (f-MLP) has been proposed, which has the first hidden layer of correlational filter operations in frequency domain. It demonstrated a superior classification performance for the pattern sets; of these, frequency information is the dominant feature for classification. The first hidden layer is implemented with the correlational filter operation; its output is the power spectrum of the filter output, while the other layers are the same as the ordinary multilayer Perceptron (o-MLP). By using the back-propagation learning algorithm for the correlational filter layer, f-MLP was able to self-adapt the filter coefficients to produce its output with more discrimination power for classification in the higher layer. In this research, this f-MLP was applied for sleep snoring signal detection. As a result, the f-MLP achieved an average detection rate of 96% for the test patterns, compared to the conventional multilayer neural network that demonstrates an 82% average detection rate.

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“…The passage of airflow through these airways should satisfy the equation of Bernoulli and the law of Poiseuille [1] but it moderately assures these laws, because of its particular anatomical and functional features. The UA has different segments characteristics which can be firm or collapsible depending on morphology and trophicity [2].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Upper Air Way Tract in Snoring and Non-Snoring Patients: A CT Based Study

Alamin¹,

Ayad²

2016

OJRad

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Snoring is part of the spectrum of sleep disordered breathing. The patients with snoring should be evaluated for nasal airway obstruction. In this study, the upper air way tract was characterized in snoring patients and compared with none snoring using a quantify method for the corresponding anatomic upper air way (UA) parameters examined by Computerized Tomography (CT). Two hundred Asian patients undergoing CT scanning for head and neck were included and were divided into two groups (snoring group [n = 127] and non-snoring group [n = 73]) (mean age: 44.8 ± 15.9 years and 26.2 ± 7.0). Total 8 parameters were measured on sagital reconstructed CT images for each subject. The differences in neck circumference (NC), linear distance between mandibular plane and hyoid bone (Mp-H), upper airway length (UAL), the maximum thickness of the soft palate (SP max), soft palate length (PNS-U), linear distance between anterior and posterior nasal spine (ANS-PNS), retroglosal width (RS), retroplatal width (RP) between the snoring and non-snoring groups were compared statistically using independent sample t-test. Results showed that the NC, UAL, and SP max were significantly higher in snoring group; however RP was found to be significantly lower. Inferior positioning of hyoid bone gives longer measurement for MP-H. In snoring group UAL was found to be significantly different at p ≤ 0.000 between the two genders. All the measured variables showed no significant differences in respect to age. UA CT quantitative features play an important role in the characterization of the anatomy and are compared between snoring patients and non-snoring subjects.

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Snoring: analysis, measurement, clinical implications and applications

Cited by 153 publications

References 49 publications

Breathing and Snoring Sound Characteristics during Sleep in Adults

Breathing and Snoring Sound Characteristics during Sleep in Adults

Sleep snoring detection using multi-layer neural networks

Characterization of Upper Air Way Tract in Snoring and Non-Snoring Patients: A CT Based Study

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