title: [Acoustic information in snoring noises].
abstract: BACKGROUND
  More than one third of all people snore regularly. Snoring is a common accompaniment of obstructive sleep apnea (OSA) and is often disruptive for the bed partner.
  
  
  OBJECTIVE
  This work gives an overview of the history of and state of research on acoustic analysis of snoring for classification of OSA severity, detection of obstructive events, measurement of annoyance, and identification of the sound excitation location.
  
  
  METHODS
  Based on these objectives, searches were conducted in the literature databases PubMed and IEEE Xplore. Publications dealing with the respective objectives according to title and abstract were selected from the search results.
  
  
  RESULTS
  A total of 48 publications concerning the above objectives were considered. The limiting factor of many studies is the small number of subjects upon which the analyses are based.
  
  
  CONCLUSIONS
  Recent research findings show promising results, such that acoustic analysis may find a place in the framework of sleep diagnostics, thus supplementing the recognized standard methods.
authors.affiliation: Zentralinstitut für Medizintechnik (IMETUM), Technische Universität München, Boltzmannstraße 11, 85748, Garching, Deutschland. c.janott@gmx.net.
authors.forename: C
authors.initials: C
authors.lastname: Janott
authors.affiliation: Lehrstuhl für Complex and Intelligent Systems, Universität Passau, Passau, Deutschland.
authors.forename: B
authors.initials: B
authors.lastname: Schuller
authors.affiliation: Hals-Nasen-Ohrenklinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, München, Deutschland.
authors.forename: C
authors.initials: C
authors.lastname: Heiser
mesh_terms: D000465:Algorithms; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D019317:Evidence-Based Medicine; D006801:Humans; D000069550:Machine Learning; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012135:Respiratory Sounds / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D012913:Snoring / Q000175:diagnosis* / Q000503:physiopathology*; D013018:Sound Spectrography / Q000379:methods*
keywords: Machine Learning; Obstructive sleep apnea; Respiration disorders; Respiratory sounds; Snoring