Skip to main content
SpringerLink
Log in

Sleep Evaluation Using Audio Signal Processing

  • Chapter
  • First Online:
Book cover Breath Sounds

Abstract

Millions of people worldwide experience sleep disorders; poor sleep is associated with increased risk for accidents, morbidity, and mortality. Early diagnosis and treatment can improve quality of life and health. However, the number of sleep testing sites is limited, and many people are left undiagnosed and untreated. The biomedical engineering field of sleep evaluation is on a “fast track” toward simple home-based testing. Existing technology for home-based sleep evaluation uses body-contact sensors that may lead to poor data acquisition and may also disturb sleep. Recently, new non-contact approaches were suggested for a simple sleep evaluation.

In this chapter, several audio-based signal processing algorithms for sleep evaluation are reviewed. These algorithms evaluate snoring and breathing, sleep-wake stages, sleep quality parameters (such as total sleep time), and obstructive sleep apnea. Our findings show that sleep and respiratory activity can be estimated by analysis of audio signals using non-contact sensors, such as microphones. The basic idea is that respiratory activity, which contains information regarding sleep stages and sleep breathing disorders, generates breathing (and snoring) sounds that can be acquired by a sensitive non-contact microphone and a digital audio recording device. Such an audio and breathing sound analysis approach may facilitate a solution for better accessibility to sleep evaluation, leading to early diagnosis and treatment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bruni O, Fabrizi P, Ottaviano S, Cortesi F, Giannotti F, Guidetti V (1997) Prevalence of sleep disorders in childhood and adolescence with headache: a case-control study. Cephalalgia 17(4):492–498

    Article  CAS  Google Scholar 

  2. Hillman DR, Murphy AS, Antic R, Pezzullo L (2006) The economic cost of sleep disorders. Sleep 29(3):299

    Article  Google Scholar 

  3. Kales JD, Healey S (1979) Prevalence of sleep disorders in the Los Angeles metropolitan area. Am J Psychiatry 36(10):1257–1262

    Google Scholar 

  4. Mastronarde JG, Wise RA, Shade DM, Olopade CO, Scharf SM, Centers ALAACR (2008) Sleep quality in asthma: results of a large prospective clinical trial. J Asthma 45(3):183–189

    Article  Google Scholar 

  5. Pillar G, Lavie P (2008) Upper airway in obstructive sleep apnea—controversies continue. J Sleep Res 17(2):123–124

    Article  Google Scholar 

  6. Ronksley PE, Hemmelgarn BR, Heitman SJ, Flemons WW, Ghali WA, Manns B, Faris P, Tsai WH (2011) Excessive daytime sleepiness is associated with increased health care utilization among patients referred for assessment of OSA. Sleep 34(3):363

    Article  Google Scholar 

  7. Sanna A (2013) Obstructive sleep apnoea, motor vehicle accidents, and work performance. Chron Respir Dis 10(1):29–33

    Article  Google Scholar 

  8. Susan Redline M, Newman AB (2002) The relationship between chronically disrupted sleep and healthcare use. Sleep 25(3):289

    PubMed  Google Scholar 

  9. Tarasiuk A, Reuveni H (2004) Obstructive sleep apnea syndrome: the diagnostic strategy dilemma. Israel Med Assoc J 6(11):686–690

    Google Scholar 

  10. Tarasiuk A, Reuveni H (2013) The economic impact of obstructive sleep apnea. Curr Opin Pulm Med 19(6):639–644

    Article  Google Scholar 

  11. Tarasiuk A, Greenberg-Dotan S, Simon T, Tal A, Oksenberg A, Reuveni H (2006) Low socioeconomic status is a risk factor for cardiovascular disease among adult obstructive sleep apnea syndrome patients requiring treatment. Chest 130(3):766–773. https://doi.org/10.1378/chest.130.3.766

    Article  PubMed  Google Scholar 

  12. Flemons WW (2002) Clinical practice. Obstructive sleep apnea. N Engl J Med 347(7):498–504. https://doi.org/10.1056/NEJMcp012849

    Article  PubMed  Google Scholar 

  13. Iber C, Ancoli-Israel S, Chesson AL, Quan S (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology, and technical specifications, 1st edn. The American Academy of Sleep Medicine, Westchester, IL

    Google Scholar 

  14. Collop N, Anderson WM, Boehlecke B, Claman D, Goldberg R, Gottlieb D, Hudgel D, Sateia M, Schwab R (2007) Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. J Clin Sleep Med 3(7):737–747

    PubMed  Google Scholar 

  15. Verginis N, Davey MJ, Horne RS (2010) Scoring respiratory events in paediatric patients: evaluation of nasal pressure and thermistor recordings separately and in combination. Sleep Med 11(4):400–405

    Article  Google Scholar 

  16. Whyte K, Gugger M, Gould G, Molloy J, Wraith P, Douglas N (1991) Accuracy of respiratory inductive plethysmograph in measuring tidal volume during sleep. J Appl Physiol 71(5):1866–1871

    Article  CAS  Google Scholar 

  17. Reuveni H, Schweitzer E, Tarasiuk A (2001) A cost-effectiveness analysis of alternative at-home or in-laboratory technologies for the diagnosis of obstructive sleep apnea syndrome. Med Decis Mak 21(6):451–458

    Article  Google Scholar 

  18. Abeyratne UR, Karunajeewa AS Hukins C (2004) Higher-order spectra for the estimation of total-airway-response (TAR) in snore-based diagnosis of apnoea. In: Control, automation, robotics and vision conference. ICARCV. IEEE, pp 925–930

    Google Scholar 

  19. Azarbarzin A, Moussavi Z (2012) Snoring sounds variability as a signature of obstructive sleep apnea. Med Eng Phys. https://doi.org/10.1016/j.medengphy.2012.06.013

  20. Colrain IM, Trinder J, Fraser G, Wilson G (1989) Ventilation during sleep onset. University of Tasmania, Tasmania

    Google Scholar 

  21. Dafna E, Tarasiuk A, Zigel Y (2012) Sleep-quality assessment from full night audio recordings of sleep apnea patients. In: Conference of the proceedings of the IEEE engineering in medicine and biology society. IEEE, pp 3660–3663

    Google Scholar 

  22. Dafna E, Tarasiuk A, Zigel Y (2015b) Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds. PLoS One 10(2):e0117382

    Article  Google Scholar 

  23. Hedner J, Pillar G, Pittman SD, Zou D, Grote L, White DP (2004) A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients. Sleep 27(8):1560–1566

    Article  Google Scholar 

  24. Marino M, Li Y, Rueschman MN, Winkelman J, Ellenbogen J, Solet J, Dulin H, Berkman LF, Buxton OM (2012) Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. Sleep 36(11):1747–1755

    Article  Google Scholar 

  25. Sadeh A, Acebo C (2002) The role of actigraphy in sleep medicine. Sleep Med Rev 6(2):113–124

    Article  Google Scholar 

  26. Scharf SM, Garshick E, Brown R, Tishler PV (1990) Screening for subclinical sleep-disordered breathing. Sleep 13(4):344–353

    CAS  PubMed  Google Scholar 

  27. Sola-Soler J, Fiz JA, Morera J, Jané R (2011) Bayes classification of snoring subjects with and without sleep apnea hypopnea syndrome, using a Kernel method. In: Engineering in Medicine and Biology Society, EMBC, 2011 annual international conference of the IEEE. IEEE, pp 6071–6074

    Google Scholar 

  28. Sola-Soler J, Jane R, Fiz JA, Morera J (2007) Automatic classification of subjects with and without sleep apnea through snoring analysis. In: Engineering in medicine and biology society, 2007. EMBS 2007. 29th Annual international conference of the IEEE. IEEE, pp 6093–6096

    Google Scholar 

  29. Berthomier C, Drouot X, Herman-Stoïca M, Berthomier P, Prado J, Bokar-Thire D, Benoit O, Mattout J, d'Ortho M-P (2007) Automatic analysis of single-channel sleep EEG: validation in healthy individuals. Sleep 30(11):1587

    Article  Google Scholar 

  30. Choi JH, Kim EJ, Kim YS, Choi J, Kim TH, Kwon SY, Lee HM, Lee SH, Shin C, Lee SH (2010) Validation study of portable device for the diagnosis of obstructive sleep apnea according to the new AASM scoring criteria: Watch-PAT 100. Acta Otolaryngol 130(7):838–843

    Article  Google Scholar 

  31. Hwang S, Chung G, Lee J, Shin J, Lee S-J, Jeong D-U, Park K (2012) Sleep/wake estimation using only anterior tibialis electromyography data. Biomed Eng Online 11:1–15

    Article  Google Scholar 

  32. Sommermeyer D, Schwaibold M, Schöller B, Grote L, Hedner J Bolz A 2010 Detection of sleep disorders by a modified matching pursuit algorithm. In: World congress on medical physics and biomedical engineering, 7–12, 2009, Munich. Springer, pp 1271–1274

    Google Scholar 

  33. Akhter S, Abeyratne U, Swarnkar V (2013) Variations of snoring properties with macro sleep stages in a population of obstructive sleep apnea patients. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th annual international conference of the IEEE. IEEE, pp 1318–1321

    Google Scholar 

  34. Ben-Israel N, Tarasiuk A, Zigel Y (2010) Nocturnal sound analysis for the diagnosis of obstructive sleep apnea. Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society conference 2010:6146–6149. https://doi.org/10.1109/IEMBS.2010.5627784

    Article  Google Scholar 

  35. Dafna E, Halevi M, Ben Or D, Tarasiuk A, Zigel Y (2016) Estimation of macro sleep stages from whole night audio analysis. In: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th annual international conference of the IEEE, pp 2847–2850

    Google Scholar 

  36. Dafna E, Tarasiuk A, Zigel Y (2013a) Automatic detection of whole night snoring events using non-contact microphone. PLoS One 8(12):e84139

    Article  Google Scholar 

  37. De Chazal P, Fox N, O’HARE E, Heneghan C, Zaffaroni A, Boyle P, Smith S, O’CONNELL C, McNicholas WT (2011) Sleep/wake measurement using a non-contact biomotion sensor. J Sleep Res 20(2):356–366

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  39. Maimon N, Hanly PJ (2010) Does snoring intensity correlate with the severity of obstructive sleep apnea. J Clin Sleep Med 6(5):475–478

    PubMed  PubMed Central  Google Scholar 

  40. Itamar Medical Ltd. http://www.itamar-medical.com/

  41. Levartovsky A, Dafna E, Zigel Y, Tarasiuk A (2016) Breathing and snoring sound characteristics during sleep in adults. J Clin Sleep Med 12(3):375–384

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  43. Dafna E, Rosenwein T, Tarasiuk A, Zigel Y (2015) Breathing rate estimation during sleep using audio signal analysis. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 5981–5984

    Google Scholar 

  44. Dafna E, Tarasiuk A, Zigel Y (2013) OSA severity assessment based on sleep breathing analysis using ambient microphone. In: 2013 35th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 2044–2047

    Google Scholar 

  45. Rosenwein T, Dafna E, Tarasiuk A, Zigel Y (2015) Breath-by-breath detection of apneic events for OSA severity estimation using non-contact audio recordings. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 7688–7691

    Google Scholar 

  46. Ben Or D, Dafna E, Tarasiuk A, Zigel Y (2016) Obstructive sleep apnea severity estimation: fusion of speech-based systems. In: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th annual international conference of the IEEE, pp 3207–3210

    Google Scholar 

  47. Elisha O, Zigel Y, Tarasiuk A (2012) Automatic detection of obstructive sleep apnea using speech signal analysis. In: Afeka-AVIOS speech processing conference

    Google Scholar 

  48. Goldshtein E, Tarasiuk A, Zigel Y (2011) Automatic detection of obstructive sleep apnea using speech signals. IEEE Trans Biomed Eng 58(5):1373–1382

    Article  Google Scholar 

  49. Kriboy M, Tarasiuk A, Zigel Y (2014) Detection of Obstructive sleep apnea in awake subjects by exploiting body posture effects on the speech signal. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, 2014, IEEE, pp 4224–4227

    Google Scholar 

  50. Rosenwein T, Dafna E, Tarasiuk A, Zigel Y (2014) Detection of breathing sounds during sleep using non-contact audio recordings. 35th Annual international IEEE EMBS conference, Chicago, IL

    Google Scholar 

  51. Sadeh A (2011) The role and validity of actigraphy in sleep medicine: an update. Sleep Med Rev 15(4):259–267

    Article  Google Scholar 

  52. Johnson NL, Kirchner HL, Rosen CL, Storfer-lsser A, Cartar LN, Ancoli-Israel S, Emancipator JL, Kibler AM, Redline S (2007) Sleep estimation using wrist actigraphy in adolescents with and without sleep disordered breathing: a comparison of three data modes. Sleep 30(7):899

    Article  Google Scholar 

  53. Blackwell T, Redline S, Ancoli-Israel S, Schneider JL, Surovec S, Johnson NL, Cauley JA, Stone KL, Group SoOFR (2008) Comparison of sleep parameters from actigraphy and polysomnography in older women: the SOF study. Sleep 31(2):283

    Article  Google Scholar 

  54. Tilmanne J, Urbain J, Kothare MV, Wouwer AV, Kothare SV (2009) Algorithms for sleep–wake identification using actigraphy: a comparative study and new results. J Sleep Res 18(1):85–98

    Article  Google Scholar 

  55. Malhotra A, Huang Y, Fogel R, Lazic S, Pillar G, Jakab M, Kikinis R, White DP (2006) Aging influences on pharyngeal anatomy and physiology: the predisposition to pharyngeal collapse. Am J Med 119(1):72.e79

    Article  Google Scholar 

  56. Pillar G, Malhotra A, Fogel RB, Beauregard J, Slamowitz DI, Shea SA, White DP (2000) Upper airway muscle responsiveness to rising PCO 2 during NREM sleep. J Appl Physiol 89(4):1275–1282

    Article  CAS  Google Scholar 

  57. Trinder J, Kay A, Kleiman J, Dunai J (1997) Gender differences in airway resistance during sleep. J Appl Physiol 83(6):1986–1997

    Article  CAS  Google Scholar 

  58. Trinder J, Whitworth F, Kay A, Wilkin P (1992) Respiratory instability during sleep onset. J Appl Physiol 73:2462–2462

    Article  CAS  Google Scholar 

  59. Edwards BA, White DP (2011) Control of the pharyngeal musculature during wakefulness and sleep: implications in normal controls and sleep apnea. Head Neck 33(S1):S37–S45

    Article  Google Scholar 

  60. Worsnop C, Kay A, Kim Y, Trinder J, Pierce R (2000) Effect of age on sleep onset-related changes in respiratory pump and upper airway muscle function. J Appl Physiol 88(5):1831–1839

    Article  CAS  Google Scholar 

  61. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Article  Google Scholar 

  62. Malik V, Smith D (2012) Respiratory physiology during sleep. Sleep Med Clin 7(3):497–505

    Article  Google Scholar 

  63. Phillipson EA (1978) Respiratory adaptations in sleep. Annu Rev Physiol 40(1):133–156

    Article  CAS  Google Scholar 

  64. Halevi M, Dafna E, Tarasiuk A, Zigel Y (2016) Can we discriminate between apnea and hypopnea using audio signals? In: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th annual international conference of the IEEE, pp 3211–3214

    Google Scholar 

  65. Montazeri A, Giannouli E, Moussavi Z (2012) Assessment of obstructive sleep apnea and its severity during wakefulness. Ann Biomed Eng 40(4):916–924

    Article  Google Scholar 

  66. Solà-Soler J, Fiz JA, Torres A, Jané R (2014) Identification of obstructive sleep apnea patients from tracheal breath sound analysis during wakefulness in polysomnographic studies. In: 2014 36th Annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 4232–4235

    Google Scholar 

  67. Espinoza-Cuadros F, Fernández-Pozo R, Toledano DT, Alcázar-Ramírez JD, López-Gonzalo E, Hernández-Gómez LA (2016) Reviewing the connection between speech and obstructive sleep apnea. Biomed Eng Online 15(1):1

    Article  Google Scholar 

  68. Saboisky JP, Butler JE, Gandevia SC, Eckert DJ (2012) Functional role of neural injury in obstructive sleep apnea. Front Neurol 3:95

    Article  Google Scholar 

  69. Fox AW, Monoson PK, Morgan CD (1989) Speech dysfunction of obstructive sleep apnea. A discriminant analysis of its descriptors. Chest J 96(3):589–595

    Article  CAS  Google Scholar 

  70. Smith L, Schwartz AR (2012) Effect of end-expiratory lung volume on upper airway. J Appl Physiol 113(5):691–699

    Article  Google Scholar 

  71. Yadollahi A, Moussavi Z (2007) Acoustical flow estimation: review and validation. IEEE Eng Med Biol Mag 26(1):56–61

    Article  Google Scholar 

  72. Alshaer H, Levchenko A, Bradley TD, Pong S, Tseng W-H, Fernie GR (2013) A system for portable sleep apnea diagnosis using an embedded data capturing module. J Clin Monit Comput 27(3):303–311

    Article  Google Scholar 

  73. Nakano H, Ikeda T, Hayashi M, Ohshima E, Onizuka A (2003) Effects of body position on snoring in apneic and nonapneic snorers. Sleep 26(2):169–176

    Article  Google Scholar 

  74. Joachim B, Aoife R, João SD, Elnaz G, Gari DC (2013) A review of current sleep screening applications for smartphones. Physiol Meas 34(7):R29

    Article  Google Scholar 

  75. Holmes MS, D'Arcy S, Costello RW, Reilly RB (2014) Acoustic analysis of inhaler sounds from community-dwelling asthmatic patients for automatic assessment of adherence. IEEE Trans Health Med 2:2700210

    Google Scholar 

  76. Taylor TE, De Looze C, MacHale P, Holmes MS, Sulaiman I, Costello RW, Reilly RB (2016a) Characterization of patient inhaler inhalation sounds using non-contact and tracheal microphones. Biomed Phys Eng Express 2(5):055021

    Article  Google Scholar 

  77. Taylor TE, Holmes MS, Sulaiman I, Costello RW, Reilly RB (2016b) Monitoring inhaler inhalations using an acoustic sensor proximal to inhaler devices. J Aerosol Med Pulm Drug Deliv 29(5):439–446. https://doi.org/10.1089/jamp.2015.1276

    Article  PubMed  Google Scholar 

  78. Zigel Y, Goldbart A, Freud T, Erew A, Abu Leil M, Tocker Y, Levi I, Urkin J (2016) Diurnal and seasonal variation of cough episodes in healthy young adults. J Asthma 53(3):295–300

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Engineering, Ben-Gurion University of the Negev, Beer-sheva, Israel

    Yaniv Zigel & Eliran Dafna

  2. Sleep-Wake Disorders Unit, Soroka University Medical Center, Beersheba, Israel

    Ariel Tarasiuk

  3. Department of Physiology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beersheba, Israel

    Ariel Tarasiuk

Authors
  1. Yaniv Zigel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ariel Tarasiuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eliran Dafna
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaniv Zigel .

Editor information

Editors and Affiliations

  1. Children’s Respiratory and Allergy Unit, Third Department of Paediatrics “Attikon” Hospital, National and Kapodistrian University of Athens, Athens, Greece

    Kostas N. Priftis

  2. Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Leontios J. Hadjileontiadis

  3. Division of Child Health, University of Western Australia, Perth Children’s Hospital, Perth, Australia

    Mark L. Everard

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zigel, Y., Tarasiuk, A., Dafna, E. (2018). Sleep Evaluation Using Audio Signal Processing. In: Priftis, K., Hadjileontiadis, L., Everard, M. (eds) Breath Sounds. Springer, Cham. https://doi.org/10.1007/978-3-319-71824-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-71824-8_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71823-1

  • Online ISBN: 978-3-319-71824-8

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation