Acoustic analysis of snoring before and after palatal surgery

Jones, Terry M.; Swift, A. C.; Calverley, PM; Ho, Meau-Shin; Earis, J. E.

doi:10.1183/09031936.05.00101703

Cited by 28 publications

(19 citation statements)

References 25 publications

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“…11, open bars). Another application for snore detection is to diagnose SDB syndromes [20], [27], [30], [48], the effectiveness of palatal surgeries regarding snores, OSA [18], and even exploration of breathing patterns during sleep time [33].…”

Section: Discussionmentioning

confidence: 99%

“…However, due to difficulties associated with PSG, such as its long waiting list and costs, there is an urgent need for simple and reliable technology for snore detection and analysis. Audio signal analysis of snore sounds can be deployed in different tasks, such as assessment of the outcome of surgical treatment [17], [18]. Recently several papers have proposed OSA detection systems [19] and apnea-hypopnea index (AHI) estimation based on whole-night audio recording of snoring [20], [21].…”

Section: Introductionmentioning

confidence: 99%

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

2013

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ObjectiveAlthough 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.DesignSounds 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.PatientsSixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m2, 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 ResultsTo 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).ConclusionsAudio-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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2013

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“…The algorithm had a SN = 90.2% and a PPV = 98.7% for simple snorers, and a SN = 86.8% and a PPV = 93.8% for OSA patients. Jones et al (Jones et al 2005, Jones, Walker, Ho, Earis, Swift & Charters 2006, Jones, Ho, Earis & Swift 2006) studied a number of acoustic features: snore duration, snore loudness, snore periodicity and sub-band energy distribution. There were 20 patients involved in this study (18 males, 2 females; age = 46 (33–65) yrs ; BMI = 31.6 (26.9–44.1) kg/m 2 ).…”

Section: Signal Processingmentioning

confidence: 99%

A review of signals used in sleep analysis

et al. 2013

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This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.

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“…The first is a complex waveform with a low frequency sound that is generated as a result of the collision of opposing airway walls during passing periods of airway obstruction. The second is a simple waveform sound with a quasi-sinusoidal pattern that could be considered as a result of the airway walls’ vibration around a neutral position without an obstruction of the respiratory tract lumen [19]. To obtain a valuable insight using the untapped reserves of the analysis method, we attempted to extract the cyclostationary properties from the sleep breathing sounds.…”

Section: Methodsmentioning

confidence: 99%

Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification

Kim

Lee

et al. 2017

BioMed Eng OnLine

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BackgroundPolysomnography (PSG) is the gold standard test for obstructive sleep apnea (OSA), but it incurs high costs, requires inconvenient measurements, and is limited by a one-night test. Thus, a repetitive OSA screening test using affordable data would be effective both for patients interested in their own OSA risk and in-hospital PSG. The purpose of this research was to develop a four-OSA severity classification model using a patient’s breathing sounds.MethodsBreathing sounds were recorded from 83 subjects during a PSG test. There was no exclusive experimental protocol or additional recording instruments use throughout the sound recording procedure. Based on the Apnea-Hypopnea Index (AHI), which indicates the severity of sleep apnea, the subjects’ sound data were divided into four-OSA severity classes. From the individual sound data, we proposed two novel methods which were not attempted in previous OSA severity classification studies. First, the total transition probability of approximated sound energy in time series, and second, the statistical properties derived from the dimension-reduced cyclic spectral density. In addition, feature selection was conducted to achieve better results with a more relevant subset of features. Then, the classification model was trained using support vector machines and evaluated using leave-one-out cross-validation.ResultsThe overall results show that our classification model is better than existing multiple OSA severity classification method using breathing sounds. The proposed method demonstrated 79.52% accuracy for the four-class classification task. Additionally, it demonstrated 98.0% sensitivity, 75.0% specificity, and 92.78% accuracy for OSA subject detection classification with AHI threshold 5.ConclusionsThe results show that our proposed method can be used as part of an OSA screening test, which can provide the subject with detailed OSA severity results from only breathing sounds.

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Acoustic analysis of snoring before and after palatal surgery

Cited by 28 publications

References 25 publications

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

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

A review of signals used in sleep analysis

Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification

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