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

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)...

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Vydané v:PloS one Ročník 8; číslo 12; s. e84139
Hlavní autori: Dafna, Eliran, Tarasiuk, Ariel, Zigel, Yaniv
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 31.12.2013
Public Library of Science (PLoS)
Predmet:
Acoustic noise
Acoustics
Acoustics - instrumentation
Adult
Aged
Aged, 80 and over
Algorithms
Amplifiers, Electronic
Analysis
Apnea
Bedding
Biology
Biomedical engineering
Body mass
Design
Detection equipment
Engineering
Feature extraction
Female
Follow-Up Studies
Humans
Laboratories
Machine learning
Male
Medicine
Methods
Middle Aged
Night
Noise
Patients
Polysomnography
Principal components analysis
Prognosis
Prospective Studies
Respiration
Signal processing
Signal Processing, Computer-Assisted
Sleep
Sleep apnea
Sleep Apnea, Obstructive - complications
Sleep disorders
Snoring - diagnosis
Snoring - etiology
Socioeconomic factors
Sound
Sound Spectrography
Studies
Validation Studies as Topic
Young Adult
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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. 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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SourceType-Scholarly Journals-1
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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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0084139