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Akustische Informationen von Schnarchgeräuschen

Acoustic information in snoring noises

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Zusammenfassung

Hintergrund

Mehr als ein Drittel aller Menschen schnarchen regelmäßig. Schnarchen ist ein häufiges Begleitsymptom einer obstruktiven Schlafapnoe (OSA) und wirkt zusätzlich störend auf den Bettpartner.

Ziel der Arbeit

Diese Arbeit gibt eine Übersicht über die Historie und den Stand der Forschung hinsichtlich der akustischen Analyse des Schnarchens zur Klassifizierung des OSA-Schweregrads, zur Detektion obstruktiver Ereignisse, zur Messung der Lästigkeit und zur Identifikation des Schallentstehungsorts.

Material und Methoden

Mit Blick auf die genannten Zielsetzungen wurden Recherchen in den Literaturdatenbanken PubMed und IEEE Xplore durchgeführt und aus den Suchergebnissen diejenigen Publikationen ausgewählt, die sich laut Titel und Abstract mit der jeweiligen Zielstellung befassen.

Ergebnisse

Es wurden insgesamt 48 Publikationen zu den genannten Zielstellungen berücksichtigt. Limitierender Faktor vieler Arbeiten ist die geringe Anzahl der Probanden, auf denen die Untersuchungen basieren.

Schlussfolgerung

Jüngere Forschungsergebnisse zeigen vielversprechende Ergebnisse, sodass akustische Analysen in der Zukunft einen Platz im Rahmen der Schlafdiagnostik als Ergänzung der anerkannten Standardverfahren finden können.

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.

Materials and 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.

Conclusion

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.

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Literatur

  1. Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S (1993) The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 328:1230–1235

    Article  CAS  PubMed  Google Scholar 

  2. Aldrich M (1999) Sleep medicine. Oxford University Press, New York/Oxford

    Google Scholar 

  3. Blumen M, Quera M, Vaugier I, Leroux K, d’Ortho M, Barbot F, Chabolle F, Lofaso F (2012) Snoring intensity responsible for the sleep partner’s poor quality of sleep? Sleep Breath 16:903–907

    Article  PubMed  Google Scholar 

  4. Stuck B, Dreher A, Heiser C, Herzog M, Kühnel T, Maurer J, Pistner H, Sitter H, Steffen A, Verse T (2013) Sk2 guidelines diagnosis and therapy of snoring in adults compiled by the sleep medicine working group of the German Society of Otorhinolaryngology, Head and Neck Surgery. HNO 61:944–957. doi:10.1007/s00106-013-2775-3

    Article  CAS  PubMed  Google Scholar 

  5. Pevernagie D, Aarts R, De Meyer M (2010) The acoustics of snoring. Sleep Med Rev 14:131–144

    Article  PubMed  Google Scholar 

  6. Pschyrembel W (2007) Klinisches Wörterbuch

    Google Scholar 

  7. Norman M, Middleton S, Erskine O, Middleton P, Wheatley J, Sullivan C (2014) Validation of the Sonomat: a contactless monitoring system used for the diagnosis of sleep disordered breathing. Sleep 37(9):1477–1487. doi: 10.5665/sleep.3996.

    PubMed  PubMed Central  Google Scholar 

  8. Rohrmeier C, Herzog M, Ettl T, Kuehnel T (2014) Distinguishing snoring sounds from breath sounds: a straightforward matter? Sleep Breath 18:169–176

    Article  PubMed  Google Scholar 

  9. Dafna E, Tarasiuk A, Zigel Y (2013) Automatic detection of whole night snoring events using non-contact microphone. PLOS ONE 8:e84139

    Article  PubMed  PubMed Central  Google Scholar 

  10. Rousselot A (1904) Principes de Phonétique expérimentale Bd. 2. Didier, Paris

    Google Scholar 

  11. Sullivan C, Issa F, Berthon-Jones M, Eves L (1981) Reversal of obstructive sleep apnoea by continuous positive airway pressure applied through the nares. Lancet 317:862–865

    Article  Google Scholar 

  12. Cohen A, Lieberman A (1986) Analysis and classification of snoring signals. Acoustics, Speech, and Signal Processing. IEEE International Conference on ICASSP, S 693–696

    Google Scholar 

  13. Schäfer J (1989) How can one recognize a velum snorer? Laryngorhinootologie 68:290–294

    Article  PubMed  Google Scholar 

  14. Janott C, Pirsig W, Heiser C (2014) Akustische Analyse von Schnarchgeräuschen. Somnologie 18:87–95

    Article  Google Scholar 

  15. American Academy of Sleep Medicine (1999) Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. The Report of an American Academy of Sleep Medicine Task Force. Sleep 22(5):667–689

    Google Scholar 

  16. Bishop M (2006) Pattern Recognition and Machine Learning. Springer, Heidelberg

    Google Scholar 

  17. Basheer I, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Article  CAS  PubMed  Google Scholar 

  18. Vapnik V (2013) The Nature of Statistical Learning Theory. Springer Science, New York

    Google Scholar 

  19. Sola-Soler J, Jane R, Fiz J, Morera J (2000) Towards automatic pitch detection in snoring signals. Engineering in Medicine and Biology Society. Proceedings of the 22nd Annual International Conference of the IEEE, S 2974–2976

    Google Scholar 

  20. Jane R, Sola-Soler J, Fiz J, Morera J (2000) Automatic detection of snoring signals: validation with simple snorers and OSAS patients. Engineering in Medicine and Biology Society. Proceedings of the 22nd Annual International Conference of the IEEE, S 3129–3131

    Google Scholar 

  21. Sola-Soler J, Jane R, Fiz J, Morera J (2003) Spectral envelope analysis in snoring signals from simple snorers and patients with Obstructive Sleep Apnea. Engineering in Medicine and Biology Society. Proceedings of the 25th Annual International Conference of the IEEE, S 2527–2530

    Google Scholar 

  22. Abeyratne U, Patabandi C, Puvanendran K (2001) Pitch-jitter analysis of snoring sounds for the diagnosis of sleep apnea. Engineering in Medicine and Biology Society. Proceedings of the 23rd Annual International Conference of the IEEE, S 2072–2075

    Google Scholar 

  23. Wakwella A, Abeyratne U, Hukins C (2004) Snore based systems for the diagnosis of apnoea: a novel feature and its receiver operating characteristics for a full-night clinical database. IEEE International Works on Biomedical Circuits and Systems, S S2/3–S5–8

    Google Scholar 

  24. Sola-Soler J, Jane R, Fiz J, Morera J (2005) Variability of snore parameters in time and frequency domains in snoring subjects with and without Obstructive Sleep Apnea. IEEE Engineering in Medicine and Biology 27th Annual Conference, S 2583–2586

    Google Scholar 

  25. Ng A, Koh T, Baey E, Puvanendran K (2006) Speech-like Analysis of Snore Signals for the Detection of Obstructive Sleep Apnea. International Conference on Biomedical and Pharmaceutical Engineering, S 99–103

    Google Scholar 

  26. Sola-Soler J, Jane R, Fiz J, Morera J (2007) Automatic classification of subjects with and without Sleep Apnea through snoring analysis. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, S 6093–6096

    Google Scholar 

  27. Karunajeewa A, Abeyratne U, Hukins C (2011) Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome. Physiol Meas 32:83–97

    Article  PubMed  Google Scholar 

  28. Matsiki D, Deligianni X, Vlachogianni-Daskalopoulou E, Hadjileontiadis L (2007) Wavelet-based Analysis of Nocturnal Snoring in Apneic Patients Undergoing Polysomnography. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, S 1912–1915

    Google Scholar 

  29. Tagluk M, Akin M, Sezgin N (2009) Time-frequency analysis of snoring sounds in patients with simple snoring and OSAS. IEEE 17th Signal Processing and Communications Applications Conference, S 293–296

    Google Scholar 

  30. Alshaer H, Rudzicz F, Falk T, Tseng W, Bradley T (2013) Classification of vibratory patterns of the upper airway during sleep. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), S 2080–2083

    Google Scholar 

  31. Kizilkaya M, Ari F, Demi̇rgünes D (2013) Detection of sleep apnea with chaotic sound features. Signal Processing and Communications Applications Conference (SIU), S 1–4

    Google Scholar 

  32. Nakano H, Hirayama K, Sadamitsu Y, Toshimitsu A, Fujita H, Shin S, Tanigawa T (2014) Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: proof of concept. J Clin Sleep Med 10(1):73–78. doi:10.5664/jcsm.3364

    PubMed  PubMed Central  Google Scholar 

  33. Ng A, Koh T (2008) Using psychoacoustics of snoring sounds to screen for obstructive sleep apnea. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, S 1647–1650

    Google Scholar 

  34. Karci E, Dogrusoz Y, Ciloglu T (2011) Detection of post apnea sounds and apnea periods from sleep sounds. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, S 6075–6078

    Google Scholar 

  35. Ng A, Koh T, Baey E, Lee T, Abeyratne U, Puvanendran K (2008) Could formant frequencies of snore signals be an alternative means for the diagnosis of obstructive sleep apnea? Sleep Med 9:894–898

    Article  PubMed  Google Scholar 

  36. Sola-Soler J, Jane R, Fiz J, Morera J (2008) Formant frequencies of normal breath sounds of snorers may indicate the risk of Obstructive Sleep Apnea Syndrome. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, S 3500–3503

    Google Scholar 

  37. Herath D, Abeyratne U, Hukins C (2015) Hidden Markov modelling of intra-snore episode behavior of acoustic characteristics of obstructive sleep apnea patients. Physiol Meas 36:2379–2404

    Article  PubMed  Google Scholar 

  38. Ben-Israel N, Tarasiuk A, Zigel Y (2012) Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults. Sleep 35(9):1299–1305. doi:10.5665/sleep.2092

    PubMed  PubMed Central  Google Scholar 

  39. Fiz J, Jané R, Solà-Soler J, Abad J, García M, Morera J (2010) Continuous analysis and monitoring of snores and their relationship to the apnea-hypopnea index. Laryngoscope 120:854–862

    Article  PubMed  Google Scholar 

  40. de Silva S, Abeyratne U, Hukins C (2012) Gender dependant snore sound based multi feature obstructive sleep apnea screening method. Conf Proc IEEE Eng Med Biol Soc, S 6353–6356

    Google Scholar 

  41. de Silva S, Abeyratne U, Hukins C (2012) Impact of gender on snore-based obstructive sleep apnea screening. Physiol Meas 33:587–601

    Article  PubMed  Google Scholar 

  42. Abeyratne U, de Silva S, Hukins C, Duce B (2013) Obstructive sleep apnea screening by integrating snore feature classes. Physiol Meas 34:99–121

    Article  CAS  PubMed  Google Scholar 

  43. Perez-Padilla J, Slawinski E, Difrancesco L, Feige R, Remmers J, Whitelaw W (1993) Characteristics of the snoring noise in patients with and without occlusive sleep apnea. Am Rev Respir Dis 147:635–644

    Article  CAS  PubMed  Google Scholar 

  44. Yang Y, Qin Y, Haung W, Peng H, Xu H (2012) Acoustic characteristics of snoring sound in patients with obstructive sleep apnea hypopnea syndrome. Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 26(8):360–363

    PubMed  Google Scholar 

  45. Yadollahi A, Giannouli E, Moussavi Z (2010) Sleep apnea monitoring and diagnosis based on pulse oximetry and tracheal sound signals. Med Biol Eng Comput 48:1087–1097

    Article  PubMed  Google Scholar 

  46. Hoffstein V, Mateika S, Anderson D (1994) Snoring: is it in the ear of the beholder? Sleep 17(6):522–526

    CAS  PubMed  Google Scholar 

  47. Caffier P, Berl J, Muaggli A, Reinhardt A, Jakob A, Möser M, Fietze I, Scherer H, Hölzl M (2007) Snoring noise pollution-the need for objective quantification of annoyance, regulatory guidelines and mandatory therapy for snoring. Physiol Meas 28:25–40

    Article  CAS  PubMed  Google Scholar 

  48. Rohrmeier C, Herzog M, Haubner F, Kuehnel T (2012) The annoyance of snoring and psychoacoustic parameters: a step towards an objective measurement. Eur Arch Otorhinolaryngol 269:1537–1543

    Article  PubMed  Google Scholar 

  49. Fastl H (2005) Psycho-Acoustics and Sound Quality. Communication Acoustics, S 139–162

    Google Scholar 

  50. Fischer R, Kuehnel T, Merz A, Ettl T, Herzog M, Rohrmeier C (2016) Calculating annoyance: an option to proof efficacy in ENT treatment of snoring? Eur Arch Otorhinolaryngol 273:4607–4613

    Article  PubMed  Google Scholar 

  51. Quinn S, Huang L, Ellis P, Williams J (1996) The differentiation of snoring mechanisms using sound analysis. Clin Otolaryngol Allied Sci 21:119–123

    Article  CAS  PubMed  Google Scholar 

  52. Hill P, Lee B, Osborne J, Osman E (1999) Palatal snoring identified by acoustic crest factor analysis. Physiol Meas 20:167–174

    Article  CAS  PubMed  Google Scholar 

  53. Beeton R, Wells, Ebden P, Whittet H, Clarke J (2007) Snore site discrimination using statistical moments of free field snoring sounds recorded during sleep nasendoscopy. Physiol Meas 28:1225–1236

    Article  CAS  PubMed  Google Scholar 

  54. Herzog M, Plößl S, Glien A, Herzog B, Rohrmeier C, Kühnel T, Plontke S, Kellner P (2015) Evaluation of acoustic characteristics of snoring sounds obtained during drug-induced sleep endoscopy. Sleep Breath 19:1011–1019

    Article  PubMed  Google Scholar 

  55. Qian K, Janott C, Zhang Z, Heiser C, Schuller B (2016) Wavelet features for classification of VOTE snore sounds. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S 221–225

    Google Scholar 

  56. Kezirian E, Hohenhorst W, de Vries N (2011) Drug-induced sleep endoscopy: the vote classification. Eur Arch Otorhinolaryngol 268:1233–1236

    Article  PubMed  Google Scholar 

  57. Schmitt M, Janott C, Pandit V, Qian K, Heiser C, Hemmert W, Schuller B (2016) A Bag-of-AudioWords Approach for Snore Sounds’ Excitation Localisation. Proceedings 14. ITG Conference on Speech Communication, IEEE, S 1–5

    Google Scholar 

  58. Hess W (1983) Pitch Determination of Speech Signals. Springer, Berlin Heidelberg

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. Zentralinstitut für Medizintechnik (IMETUM), Technische Universität München, Boltzmannstraße 11, 85748, Garching, Deutschland

    C. Janott

  2. Lehrstuhl für Complex and Intelligent Systems, Universität Passau, Passau, Deutschland

    B. Schuller

  3. Hals-Nasen-Ohrenklinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, München, Deutschland

    C. Heiser

Authors
  1. C. Janott
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  2. B. Schuller
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  3. C. Heiser
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Correspondence to C. Janott.

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Interessenkonflikt

C. Janott ist der Erfinder eines patentierten Verfahrens und Systems zur Ermittlung anatomischer Ursachen für die Entstehung von Schnarchgeräuschen (DE102012219128B4). B. Schuller und C. Heiser geben an, dass kein Interessenkonflikt besteht.

Dieser Beitrag beinhaltet keine von den Autoren durchgeführten Studien an Menschen oder Tieren.

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Janott, C., Schuller, B. & Heiser, C. Akustische Informationen von Schnarchgeräuschen. HNO 65, 107–116 (2017). https://doi.org/10.1007/s00106-016-0331-7

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