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Breath variability increases in the minutes preceding obstructive sleep apneic events

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

Purpose

It is unclear if there is a consistent signature in breath patterns prior to an impending obstructive apneic event in patients with sleep-disordered breathing (SDB).

Objective

To use continuous recordings of ambient sound in sleep using a smartphone to track auditory signatures of breaths and measure their regularity preceding apneic events.

Methods

We studied 50 patients evaluated for SDB in whom sound was recorded using smartphones concurrently with polysomnography (PSG). Whole-night sound files were analyzed for time and frequency domain analyses of breath periodicity during periods of normal and sleep-disordered breathing.

Results

Fifty patients (44% women, 42.0 ± 9.4 years old, BMI 32.8 ± 10.8 kg/m2) recorded sound, of whom 30 were diagnosed with OSA and 20 were not. We analyzed a total of 497 apneic (≥10 s) and 481 non-apneic intervals, confirmed by PSG. Interbreath intervals were 3.75 ± 0.62 s for 1 min in quiet breathing, with SD 1.11 ± 0.48 s that increased to 4.16 ± 3.06 s in successive 60-s epochs up to apnea (p < 0.001). Interbreath SD in the 60 s immediately preceding apnea was higher than the SD in random non-apneic periods (p < 0.01, ANOVA). Interbreath SD ≥1.49 s gave 87.3% sensitivity and 86.5% specificity for predicting apnea in the next minute (c-statistic 0.94).

Conclusions

Breaths increase in variability minutes before proven obstructive apnea in patients with suspected SDB. These results suggest that it may be possible to predict and thus potentially avert apneic events and provide insights into events leading to SDB.

Trial registration

NCT03288376, clinicaltrials.org

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Abbreviations

AASM:

American Academy of Sleep Medicine

AHI:

Apnea Hypopnea Index

BMI:

body mass index (kg/m2)

CPAP:

continuous positive airway pressure

dB:

decibel

ECG:

electrocardiogram

EEG:

electroencephalogram

Epoch:

one-minute audio segment

HST:

home sleep testing

IRB:

Institutional Review Board

PSG:

polysomnography

OSA:

obstructive sleep apnea

SD:

standard deviation

SDB:

sleep-disordered breathing

References

  1. Mysliwiec V, Martin JL, Ulmer CS, Chowdhuri S, Brock MS, Spevak C and Sall J (2020) The management of chronic insomnia disorder and obstructive sleep apnea: synopsis of the 2019 U.S. Department of Veterans Affairs and U.S. Department of Defense Clinical Practice Guidelines. Ann Intern Med

  2. Pack A, Maislin G, Staley B, Pack F, Rogers W, George C, Dinges D (2006) Impaired performance in commercial drivers—role of sleep apnea and short sleep duration. American Journal of Respiratory and Critical Care Medicine 174:446–454

    Article  Google Scholar 

  3. Siedlecka J, Rybacki M, Plywaczewski R, Czajkowska-Malinowska M, Radlinski J, Kania A, Sliwinski P (2020) The management of obstructive sleep apnea syndrome in drivers—recommendations of the Polish Society Of Occupational Medicine, the Polish Respiratory Society, the Nofer Institute of Occupational Medicine in Lodz and the Polish Sleep Research Society. Med Pr 71:233–243

    PubMed  Google Scholar 

  4. Linz D, McEvoy RD, Cowie MR, Somers VK, Nattel S, Levy P, Kalman JM, Sanders P (2018) Associations of obstructive sleep apnea with atrial fibrillation and continuous positive airway pressure treatment: a review. JAMA Cardiol 3:532–540

    Article  Google Scholar 

  5. Freedman N (2015) Counterpoint: Does laboratory polysomnography yield better outcomes than home sleep testing? No. Chest; 148:308–10

  6. Pack AI (2015) Point: Does laboratory polysomnography yield better outcomes than home sleep testing? Yes. Chest;148:306–8

  7. Friedman M, Shnowske K, Hamilton C, Samuelson CG, Hirsch M, Pott TR, Yalamanchali S (2014) Mandibular advancement for obstructive sleep apnea: relating outcomes to anatomy. JAMA otolaryngology-- head & neck surgery 140:46–51

    Article  Google Scholar 

  8. Koo SK, Kwon SB, Kim YJ, Moon JI, Kim YJ and Jung SH. (2016) Acoustic analysis of snoring sounds recorded with a smartphone according to obstruction site in OSAS patients. Eur Arch Otorhinolaryngol

  9. Behbehani K, Lopez F, Yen FC, Lucas EA, Burk JR, Axe JP, Kamangar F (1997) Pharyngeal wall vibration detection using an artificial neural network. Medical & biological engineering & computing 35:193–198

    Article  CAS  Google Scholar 

  10. Tan SK, Leung WK, Tang ATH, Zwahlen RA (2017) How does mandibular advancement with or without maxillary procedures affect pharyngeal airways? An overview of systematic reviews. PLoS One 12:e0181146

    Article  Google Scholar 

  11. Narayan S, Shivdare P, Niranjan T, Williams K, Freudman J and Sehra R. (2018) Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography. Sleep Breath

  12. Ben-Israel N, Tarasiuk A and 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 Annual Conference;2010:6146–9

  13. Force USPST, Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Epling JW Jr, Garcia FA, Herzstein J, Kemper AR, Krist AH, Kurth AE, Landefeld CS, Mangione CM, Phillips WR, Phipps MG, Pignone MP, Silverstein M, Tseng CW (2017) Screening for obstructive sleep apnea in adults: US Preventive Services Task Force recommendation statement. JAMA. 317:407–414

    Article  Google Scholar 

  14. Smith HA, Smith ML (2017) The role of dentists and primary care physicians in the care of patients with sleep-related breathing disorders. Front Public Health 5:137

    Article  Google Scholar 

  15. Bailes S, Fichten CS, Rizzo D, Baltzan M, Grad R, Pavilanis A, Creti L, Amsel R, Libman E (2017) The challenge of identifying family medicine patients with obstructive sleep apnea: addressing the question of gender inequality. Fam Pract 34:467–472

    Article  Google Scholar 

  16. Chiu HY, Chen PY, Chuang LP, Chen NH, Tu YK, Hsieh YJ, Wang YC, Guilleminault C (2017) Diagnostic accuracy of the Berlin questionnaire, STOP-BANG, STOP, and Epworth sleepiness scale in detecting obstructive sleep apnea: a bivariate meta-analysis. Sleep Med Rev 36:57–70

    Article  Google Scholar 

  17. Younes M, Raneri J and Hanly P. (2016) Staging sleep in polysomnograms: analysis of inter-scorer variability. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine

  18. Majumder S, Mondal T and Deen MJ (2017) Wearable sensors for remote health monitoring. Sensors (Basel). 17

  19. Thap T, Chung H, Jeong C, Hwang KE, Kim HR, Yoon KH and Lee J. (2016) High-resolution time-frequency spectrum-based lung function test from a smartphone microphone. Sensors (Basel). 16

  20. Toy BC, Myung DJ, He L, Pan CK, Chang RT, Polkinhorne A, Merrell D, Foster D, Blumenkranz MS (2016) Smartphone-based dilated fundus photography and near visual acuity testing as inexpensive screening tools to detect referral warranted diabetic eye disease. Retina. 36:1000–1008

    Article  Google Scholar 

  21. Cornet VP, Holden RJ (2018) Systematic review of smartphone-based passive sensing for health and wellbeing. J Biomed Inform 77:120–132

    Article  Google Scholar 

  22. Agbana TE, Diehl JC, van Pul F, Khan SM, Patlan V, Verhaegen M, Vdovin G (2018) Imaging & identification of malaria parasites using cellphone microscope with a ball lens. PLoS One 13:e0205020

    Article  Google Scholar 

  23. Shcherbina A and Ashley EA. (2017) Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort. J Pers Med 7(2):3

  24. 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. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 10:73–78

    Google Scholar 

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Correspondence to Sanjiv Narayan.

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Conflict of interest

Dr. Narayan, Dr. Sehra, and Mr. Niranjan are inventors of an intellectual property owned by Resonea Inc. Dr. Sehra and Mr. Niranjan are employees of Resonea Inc.

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All participants involved in the study gave their informed consent.

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Pandian, T.N.G., Sehra, R. & Narayan, S. Breath variability increases in the minutes preceding obstructive sleep apneic events. Sleep Breath 25, 271–280 (2021). https://doi.org/10.1007/s11325-020-02094-1

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  • DOI: https://doi.org/10.1007/s11325-020-02094-1

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