Sleep and Breathing

, Volume 21, Issue 1, pp 119–133 | Cite as

Support vector machines for automated snoring detection: proof-of-concept

  • Laura B. Samuelsson
  • Anusha A. Rangarajan
  • Kenji Shimada
  • Robert T. Krafty
  • Daniel J. Buysse
  • Patrick J. Strollo
  • Howard M. Kravitz
  • Huiyong Zheng
  • Martica H. Hall
Sleep Breathing Physiology and Disorders • Original Article



Snoring has been shown to be associated with adverse physical and mental health, independent of the effects of sleep disordered breathing. Despite increasing evidence for the risks of snoring, few studies on sleep and health include objective measures of snoring. One reason for this methodological limitation is the difficulty of quantifying snoring. Conventional methods may rely on manual scoring of snore events by trained human scorers, but this process is both time- and labor-intensive, making the measurement of objective snoring impractical for large or multi-night studies.


The current study is a proof-of-concept to validate the use of support vector machines (SVM), a form of machine learning, for the automated scoring of an objective snoring signal. An SVM algorithm was trained and tested on a set of approximately 150,000 snoring and non-snoring data segments, and F-scores for SVM performance compared to visual scoring performance were calculated using the Wilcoxon signed rank test for paired data.


The ability of the SVM algorithm to discriminate snore from non-snore segments of data did not differ statistically from visual scorer performance (SVM F-score = 82.46 ± 7.93 versus average visual F-score = 88.35 ± 4.61, p = 0.2786), supporting SVM snore classification ability comparable to visual scorers.


In this proof-of-concept, we established that the SVM algorithm performs comparably to trained visual scorers, supporting the use of SVM for automated snoring detection in future studies.


Snoring Machine learning Support vector machines Automated snore detection 


Compliance with ethical standards


The Study of Women’s Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR), and the NIH Office of Research on Women’s Health (ORWH) (Grants U01NR004061, U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495). Funding for the SWAN Sleep Study is from the National Institute on Aging (Grants AG019360, AG019361, AG019362, AG019363). The NIH provided additional financial support in the form of funding to Ms. Samuelsson and Dr. Krafty (R01GM113243). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH, or the NIH.

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Kravitz HM, Ganz PA, Bromberger J, Powell LH, Sutton-Tyrrell K, Meyer PM (2003) Sleep difficulty in women at midlife: a community survey of sleep and the menopausal transition. Menopause 10:19–28PubMedGoogle Scholar
  2. 2.
    Ganguli M, Reynolds CF, Gilby JE (1996) Prevalence and persistence of sleep complaints in a rural elderly community sample: the MoVIES project. J Am Geriatr Soc 44:778–784CrossRefPubMedGoogle Scholar
  3. 3.
    Balsevicius T, Uloza V, Sakalauskas R, Miliauskas S (2012) Peculiarities of clinical profile of snoring and mild to moderate obstructive sleep apnea-hypopnea syndrome patients. Sleep Breath 16:835–843CrossRefPubMedGoogle Scholar
  4. 4.
    Ika K, Suzuki E, Mitsuhashi T, Takao S, Doi H (2013) Shift work and diabetes mellitus among male workers in Japan: does the intensity of shift work matter? Acta Med Okayama 67:25–33PubMedGoogle Scholar
  5. 5.
    Baldwin C (2010) Preventing late-life depression: a clinical update. Int Psychogeriatr 22:1216–1224CrossRefPubMedGoogle Scholar
  6. 6.
    Gates GJ, Mateika SE, Basner RC, Mateika JH (2004) Baroreflex sensitivity in nonapneic snorers and control subjects before and after nasal continuous positive airway pressure. Chest 123:801–807CrossRefGoogle Scholar
  7. 7.
    Gates GJ, Mateika SE, Mateika JH (2005) Heart rate variability in non-apneic snorers and controls before and after continuous positive airway pressure. BMC Pulm Med 5:1–9CrossRefGoogle Scholar
  8. 8.
    Mateika JH, Mateika S, Slutsky AS, Hoffstein V (1992) The effect of snoring on mean arterial blood pressure during non-REM sleep. Am Rev Respir Dis 145:141–146CrossRefPubMedGoogle Scholar
  9. 9.
    Hoffstein V, Mateika J (1992) Evening-to-morning blood pressure variations in snoring patients with and without obstructive sleep apnea. Chest 101:379–384CrossRefPubMedGoogle Scholar
  10. 10.
    D’Alessandro R, Magelli C, Gamberini G, Bacchelli S, Cristina E, Magnani B, Lugaresi E (1990) Snoring every night as a risk factor for myocardial infarction: a case-control study. BMJ 300:1557–1558CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Hu FB, Willett WC, Manson JE, Colditz GA, Rimm EB, Speizer FE, Hennekens CH, Stampfer MJ (2000) Snoring and risk of cardiovascular disease in women. J Am Coll Cardiol 35:308–313CrossRefPubMedGoogle Scholar
  12. 12.
    Palomaki H (1991) Snoring and the risk of ischemic brain infarction. Stroke 22:1021–1025CrossRefPubMedGoogle Scholar
  13. 13.
    Troxel WM, Buysse DJ, Matthews KA, Kip KE, Strollo PJ, Hall M, Drumheller O, Reis SE (2010) Sleep symptoms predict the development of the metabolic syndrome. Sleep 33:1633–1640PubMedPubMedCentralGoogle Scholar
  14. 14.
    Cathcart RA, Hamilton DW, Drinnan MJ, Gibson GJ, Wilson JA (2010) Night-to-night variation in snoring sound severity: one night studies are not reliable. Clin Otolaryngol 35:198–203CrossRefPubMedGoogle Scholar
  15. 15.
    Hoffstein V, Mateika S, Nash S (1996) Comparing perceptions and measurements of snoring. Sleep 19:783–789CrossRefPubMedGoogle Scholar
  16. 16.
    Perez-Padilla JR, West P, Kryger M (1987) Snoring in normal young adults: prevalence in sleep stages and associated changes in oxygen saturation, heart rate, and breathing pattern. Sleep 10:249–253PubMedGoogle Scholar
  17. 17.
    Lee SA, Amis TC, Byth K, Larcos G, Kairaitis K, Robinson TD, Wheatley JR (2008) Heavy snoring as a cause of carotid artery atherosclerosis. Sleep 31:1207–1213PubMedPubMedCentralGoogle Scholar
  18. 18.
    Deary V, Ellis JG, Wilson JA, Coulter C, Barclay NL (2014) Simple snoring: not quite so simple after all? Sleep Med Rev 18:453–462CrossRefPubMedGoogle Scholar
  19. 19.
    Gottlieb DJ, Yao Q, Redline S, Ali T, Mahowald MW (2000) Does snoring predict sleepiness independently of apnea and hypopnea frequency? Am J Respir Crit Care Med 162:1512–1517CrossRefPubMedGoogle Scholar
  20. 20.
    Hedner JA, Wilcox I, Sullivan CE (1994) Speculations on the interaction between vascular disease and obstructive sleep apnea. In: Saunders NA, Sullivan C (eds) Sleep and breathing. Dekker, New YorkGoogle Scholar
  21. 21.
    Stuck BA, Abrams J, de la Chaux R, Dreher A, Heiser C, Hohenhorst W, Kuhnel T, Maurer JT, Pirsig W, Steffen A, Verse T (2010) Diagnosis and treatment of snoring in adults—S1 guideline of the German Society of Otorhinolaryngology, Head and Neck Surgery. Sleep Breath 14:317–321CrossRefPubMedGoogle Scholar
  22. 22.
    Jin H, Lee LA, Song L, Li Y, Peng J, Zhong N, Li HY, Zhang X (2015) Acoustic analysis of snoring in the diagnosis of obstructive sleep apnea syndrome: a call for more rigorous studies. J Clin Sleep Med 11:765–771PubMedPubMedCentralGoogle Scholar
  23. 23.
    Kravitz HM, Zheng H, Bromberger JT, Buysse DJ, Owens J, Hall MH (2015) An actigraphy study of sleep and pain in midlife women: the SWAN sleep study. Menopause 22:710–718CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Hall M, Matthews KA, Kravitz HM, Gold EB, Buysse DJ, Bromberger JT, Owens JF, Sowers MF (2009) Race and financial strain are independent correlates of sleep in mid-life women: the SWAN sleep study. Sleep 32:73–82PubMedPubMedCentralGoogle Scholar
  25. 25.
    Monk T, Reynolds CF, Buysse DJ, Coble PA, Hayes AJ, Machen MA, Petrie SR, Ritenour AM (1994) The Pittsburgh sleep diary. J Sleep Res 3:111–120CrossRefGoogle Scholar
  26. 26.
    American Academy of Sleep Medicine Task Force (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 22:667–689Google Scholar
  27. 27.
    Issa FG, Morrison D, Hadjuk E, Iyer A, Feroah T, Remmers JE (1993) Digital monitoring of sleep-disordered breathing using snoring sound and arterial oxygen saturation. Am Rev Respir Dis 148:1023–1029CrossRefPubMedGoogle Scholar
  28. 28.
    Schwartz RS, Salome NN, Ingmundon PT, Rugh JD (1996) Effects of electrical stimulation to the soft palate on snoring and obstructive sleep apnea. J Prosthet Dent 76:273–281CrossRefPubMedGoogle Scholar
  29. 29.
    Hoffstein V, Mateika JH, Mateika S (1991) Snoring and sleep architecture. Am Rev Respir Dis 143:92–96CrossRefPubMedGoogle Scholar
  30. 30.
    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 27:1–27CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Laura B. Samuelsson
    • 1
  • Anusha A. Rangarajan
    • 2
  • Kenji Shimada
    • 3
  • Robert T. Krafty
    • 4
  • Daniel J. Buysse
    • 5
  • Patrick J. Strollo
    • 6
  • Howard M. Kravitz
    • 7
  • Huiyong Zheng
    • 8
  • Martica H. Hall
    • 5
  1. 1.Department of PsychologyUniversity of PittsburghPittsburghUSA
  2. 2.Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of Mechanical EngineeringCarnegie Mellon UniversityPittsburghUSA
  4. 4.Department of BiostatisticsUniversity of PittsburghPittsburghUSA
  5. 5.Department of PsychiatryUniversity of PittsburghPittsburghUSA
  6. 6.Division of Pulmonary, Allergy, and Critical Care MedicineUniversity of PittsburghPittsburghUSA
  7. 7.Department of Psychiatry and Department of Preventive MedicineRush UniversityChicagoUSA
  8. 8.Department of BiostatisticsUniversity of MichiganAnn ArborUSA

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