Abstract
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.
Highlights
Partial or complete collapse of the upper airway during sleep has different effects on the human body, ranging from noisy breathing [1] to obstructive sleep apnea (OSA), which can lead to considerable cardiovascular morbidity [2,3]
A feature selection process was applied to select the most discriminative features extracted from time and spectral domains
Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events
Summary
Partial or complete collapse of the upper airway during sleep has different effects on the human body, ranging from noisy breathing (simple snoring) [1] to obstructive sleep apnea (OSA), which can lead to considerable cardiovascular morbidity [2,3]. The estimated prevalence of selfreported snoring in the general population extends over a wide range from 16% to 89% [9,10,11,12,13] This prevalence depends on awareness, age, culture, and partner complaints [4,7,14]. An additional limitation of questionnaires is that a large portion of the subjects respond that they ‘‘do not know’’ if they snore [10]. To overcome these limitations, some clinicians ask the patient to supply an audio recording of their snoring, for example, prior to snore reduction surgery or to avoid operating on a ‘‘snorer’’ when the problem lies with the bed partner being disturbed by essentially normal nocturnal breathing noise [1]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
