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      Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone

      1 , 2 , 1 , *

      PLoS ONE

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          Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology.


          Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events.


          Sixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m 2, m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects.

          Measurements and Results

          To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise).


          Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.

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          Most cited references 39

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          The acoustics of snoring.

          Snoring is a prevalent disorder affecting 20-40% of the general population. The mechanism of snoring is vibration of anatomical structures in the pharyngeal airway. Flutter of the soft palate accounts for the harsh aspect of the snoring sound. Natural or drug-induced sleep is required for its appearance. Snoring is subject to many influences such as body position, sleep stage, route of breathing and the presence or absence of sleep-disordered breathing. Its presentation may be variable within or between nights. While snoring is generally perceived as a social nuisance, rating of its noisiness is subjective and, therefore, inconsistent. Objective assessment of snoring is important to evaluate the effect of treatment interventions. Moreover, snoring carries information relating to the site and degree of obstruction of the upper airway. If evidence for monolevel snoring at the site of the soft palate is provided, the patient may benefit from palatal surgery. These considerations have inspired researchers to scrutinize the acoustic characteristics of snoring events. Similarly to speech, snoring is produced in the vocal tract. Because of this analogy, existing techniques for speech analysis have been applied to evaluate snoring sounds. It appears that the pitch of the snoring sound is in the low-frequency range ( 500 Hz). To evaluate acoustic properties of snoring, sleep nasendoscopy is often performed. Recent evidence suggests that the acoustic quality of snoring is markedly different in drug-induced sleep as compared with natural sleep. Most often, palatal surgery alters sound characteristics of snoring, but is no cure for this disorder. It is uncertain whether the perceived improvement after palatal surgery, as judged by the bed partner, is due to an altered sound spectrum. Whether some acoustic aspects of snoring, such as changes in pitch, have predictive value for the presence of obstructive sleep apnea is at present not sufficiently substantiated. (c) 2009 Elsevier Ltd. All rights reserved.
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            Low socioeconomic status is a risk factor for CPAP acceptance among adult OSAS patients requiring treatment.

            To evaluate whether socioeconomic status (SES) has a role in obstructive sleep apnea syndrome (OSAS) patients' decision to accept continuous positive airway pressure (CPAP) treatment. Cross-sectional study; patients were recruited between March 2007 and December 2007. University-affiliated sleep laboratory. 162 consecutive newly diagnosed (polysomnographically) adult OSAS patients who required CPAP underwent attendant titration and a 2-week adaptation period. 40% (n = 65) of patients who required CPAP therapy accepted this treatment. Patients accepting CPAP were older, had higher apnea-hypopnea index (AHI) and higher income level, and were more likely to sleep in a separate room than patients declining CPAP treatment. More patients who accepted treatment also reported receiving positive information about CPAP treatment from family or friends. Multiple logistic regression (after adjusting for age, body mass index, Epworth Sleepiness Scale, and AHI) revealed that CPAP purchase is determined by: each increased income level category (OR, 95% CI) (2.4; 1.2-4.6), age + 1 year (1.07; 1.01-1.1), AHI ( > or = 35 vs. < 35 events/hr) (4.2, 1.4-12.0), family and/or friends with positive experience of CPAP (2.9, 1.1-7.5), and partner sleeps separately (4.3, 1.4-13.3). In addition to the already known determinants of CPAP acceptance, patients with low SES are less receptive to CPAP treatment than groups with higher SES. CPAP support and patient education programs should be better tailored for low SES people in order to increase patient treatment initiation and adherence.
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              Predictive value of clinical features in diagnosing obstructive sleep apnea.

              We examined the predictive value of history and physical examination in the diagnosis of obstructive sleep apnea (OSA) syndrome. This was achieved by studying a set of 594 patients referred to the sleep clinic because of suspicion of sleep apnea. All patients were asked a set of standard sleep-related questions and all had nocturnal polysomnography. We used stepwise multiple linear regression analysis to examine the relationship between the apnea/hypopnea index (AHI), defined as the number of episodes of cessation of breathing per hour of sleep (dependent variable), and age, sex, body mass index (BMI) and replies to the sleep questionnaire (independent variables). We found that age, sex, body mass index, bed partner observation of apnea and pharyngeal examination were significant predictors of AHI, explaining 36% of the variability. Subjective impression of the examining clinician was also an independent significant predictor of AHI, accounting for 10% of the variability. Using a conventional cutoff value of 10 to divide patients into apneics (AHI > 10) and nonapneics (AHI < or = 10), the sensitivity of subjective impression was 60% and the specificity 63%. We conclude that although clinical features obtained during history and physical examination explain a relatively high percent of the variability in AHI, subjective clinical impression alone is not sufficient to reliably identify patients with or without sleep apnea.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                31 December 2013
                : 8
                : 12
                [1 ]Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer–Sheva, Israel
                [2 ]Sleep-Wake Disorders Unit, Soroka University Medical Center, and Department of Physiology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel
                King Saud University, Saudi Arabia
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: YZ ED AT. Performed the experiments: YZ ED. Analyzed the data: ED YZ. Contributed reagents/materials/analysis tools: YZ ED. Wrote the paper: ED AT YZ. Recruitment funds: YZ AT.


                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 14
                This work was supported by the Israel Ministry of Industry, Trade and Labor - the Kamin Program, award no. 46168 to YZ and AT. These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Medical Devices
                Biomedical Engineering
                Medical Devices
                Signal Processing
                Audio Signal Processing
                Statistical Signal Processing
                Anatomy and Physiology
                Physiological Processes
                Drugs and Devices
                Medical Devices
                Sleep Disorders
                Sleep and Ventilation Disorders



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