title: Automatic detection of whole night snoring events using non-contact microphone.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
authors.forename: Eliran
authors.initials: E
authors.lastname: Dafna
authors.affiliation: Sleep-Wake Disorders Unit, Soroka University Medical Center, and Department of Physiology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel.
authors.forename: Ariel
authors.initials: A
authors.lastname: Tarasiuk
authors.affiliation: Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
authors.forename: Yaniv
authors.initials: Y
authors.lastname: Zigel
mesh_terms: D000162:Acoustics / Q000295:instrumentation*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000669:Amplifiers, Electronic; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography; D011379:Prognosis; D011446:Prospective Studies; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000150:complications*; D012913:Snoring / Q000175:diagnosis* / Q000209:etiology; D013018:Sound Spectrography; D054928:Validation Studies as Topic; D055815:Young Adult