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An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea

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Abstract

Objective

The study compares polysomnography (PSG) and cardiopulmonary coupling (CPC) sleep quality variables in patients with (1) obstructive sleep apnea (OSA) and (2) successful and unsuccessful continuous positive airway pressure (CPAP) response.

Patients/methods

PSGs from 50 subjects (32 F/18 M; mean age 48.4 ± 12.29 years; BMI 34.28 ± 9.33) were evaluated. OSA patients were grouped by no (n = 16), mild (n = 13), and moderate to severe (n = 20) OSA (apnea–hypopnea index (AHI) ≤ 5, >5–15, >15 events/h, respectively). Outcome sleep quality variables were sleep stages in non-rapid eye movement, rapid eye movement sleep, and high (HFC), low (LFC), very low-frequency coupling (VLFC), and elevated LFC broad band (e-LFCBB). An AHI ≤ 5 events/h and HFC ≥ 50 % indicated a successful CPAP response. CPC analysis extracts heart rate variability and QRS amplitude change that corresponds to respiration. CPC-generated spectrograms represent sleep dynamics from calculated coherence product and cross-power of both time series datasets.

Results

T tests differentiated no and moderate to severe OSA groups by REM % (p = 0.003), HFC (p = 0.007), VLFC (p = 0.007), and LFC/HFC ratio (p = 0.038) variables. The successful CPAP therapy group (n = 16) had more HFC (p = 0.003), less LFC (p = 0.003), and e-LFCBB (p = 0.029) compared to the unsuccessful CPAP therapy group (n = 8). PSG sleep quality measures, except the higher arousal index (p = 0.038) in the unsuccessful CPAP group, did not differ between the successful and unsuccessful CPAP groups. HFC ≥ 50 % showed high sensitivity (77.8 %) and specificity (88.9 %) in identifying successful CPAP therapy.

Conclusions

PSG and CPC measures differentiated no from moderate to severe OSA groups and HFC ≥ 50 % discriminated successful from unsuccessful CPAP therapy. The HFC ≥ 50 % cutoff showed clinical value in identifying sleep quality disturbance among CPAP users.

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Abbreviations

AASM:

American Academy of Sleep Medicine

CPAP:

Continuous positive airway pressure

CPC:

Cardiopulmonary coupling

CSA:

Central sleep apnea

ECG:

Electrocardiography

EDR:

ECG-derived respiratory signal

e-LFC BB :

Elevated low-frequency coupling broad band

HFC:

High-frequency coupling

LFC:

Low-frequency coupling

VLFC:

Very low-frequency coupling

OSA:

Obstructive sleep apnea

PSG:

Polysomnography

SD:

Sleep disorder

References

  1. Thomas RJ (2003) Arousals in sleep-disordered breathing: patterns and implications. Sleep 26(8):1042–1047

    PubMed  Google Scholar 

  2. Terzano MG, Parrino L, Boselli M, Spaggiari MC, Di Giovanni G (1996) Polysomnographic analysis of arousal responses in obstructive sleep apnea syndrome by means of the cyclic alternating pattern. J Clin Neurophysiol 13(2):145–155

    Article  PubMed  CAS  Google Scholar 

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

    Google Scholar 

  4. American Academy of Sleep Medicine (2001) ICSD—International classification of sleep disorders, revised: diagnostic and coding manual. American Academy of Sleep Medicine, Chicago

    Google Scholar 

  5. Hosselet JJ, Norman RG, Ayappa I, Rapoport DM (1998) Detection of flow limitation with a nasal cannula/pressure transducer system. Am J Respir Crit Care Med 157:1461–1467

    Article  PubMed  CAS  Google Scholar 

  6. Thomas RJ, Mietus JE, Peng CK, Gilmartin G, Daly RW, Goldberger AL, Gottlieb DJ (2007) Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method. Sleep 30:1756–1769

    PubMed  Google Scholar 

  7. Thomas R, Mietus J, Peng CK, Goldberger A (2005) An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep. Sleep 28(9):1151–1161

    PubMed  Google Scholar 

  8. Bartsch R, Schumann A, Kantelhardt J, Penzel T, Ivanov P. (2012) Phase transitions in physiologic coupling. PNAS 10181–10186.

  9. Thomas RJ, Weiss MD, Mietus JE, Peng CK, Goldberger AL (2009) Prevalent hypertension and stroke in the Sleep Heart Health Study: association with an ECG-derived spectrographic marker of cardiopulmonary coupling. Sleep 32:897–904

    PubMed  Google Scholar 

  10. Jacques L, Spencer F, Stiller J, Brechner R. (2008) Center for Medicare and Medical Services Decision Memo for continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea (CAG-00093R2); http://www.carolinasleepsociety.org/documents/cms_coverage_decision_memo_for_cpap.pdf. Accessed 22 Aug 2012

  11. Julious SA (2005) Sample size of 12 per group rule of thumb for a pilot study. Pharm Stats 4(4):287–291

    Article  Google Scholar 

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

  13. Schramm P, Thomas R, Feige B, Spiegelhalder K, Riemann D (2012) Quantitative measurement of sleep quality using cardiopulmonary coupling analysis: a retrospective comparison of individuals with and without Primary Insomnia. Sleep & Breathing. doi:10.1007/s11325-012-0747-6

  14. Yang AC, Yang CH, Hong CJ, Tsai SJ, Kuo CH, Peng CK, Mietus JE, Goldberger AL, Thomas RJ (2010) Sleep state instabilities in major depression disorder: detection and quantification with electrocardiogram-based cardiopulmonary coupling analysis. Psychophysiology. doi:10.1111/j.1469-8986.2010.01060.x

  15. Schramm PJ, Thomas RJ (2012) Assessment of therapeutic options for mild obstructive sleep apnea using cardiopulmonary coupling measures. J Clin Sleep Med 8(3):315–320

    PubMed  Google Scholar 

  16. Thomas RJ, Mietus JE, Peng CK, Goldberger AL, Crofford LJ, Chervin RD (2010) Impaired sleep quality in fibromyalgia: detection and quantification with ECG-based cardiopulmonary coupling spectrograms. Sleep Med 11(5):497–498

    Article  PubMed  Google Scholar 

  17. Basner M, Griefahn B, Müller U, Plath G, Samel A (2007) An ECG-based algorithm for the automatic identification of autonomic activations associated with cortical arousal. Sleep 30(10):1349–1361

    PubMed  Google Scholar 

  18. Manconi M, Vitale G, Ferri R, Zucconi M, Ferini-Strambi L (2008) Periodic leg movements in Cheyne–Stokes respiration. Eur Resp J 32:1656–1662

    Article  CAS  Google Scholar 

  19. Lopes C, Guilleminault C (2006) Chronic snoring and sleep in children: a demonstration of sleep disruption. Pediatrics 118:741–746

    Article  Google Scholar 

  20. Spiegelhalder K, Fuchs L, Ladwig J, Kyle S, Nissen C, Voderholzer U, Feige B, Riemann D (2010) Heart rate and heart rate variability in subjectively reported insomnia. J Sleep Res 20:137–145

    Article  Google Scholar 

  21. Feige B, Al-Shajlawi A, Nissen C, Voderholzer U, Hornyak M, Spiegelhalder K, Kloepfer C, Perlis M, Riemann D (2008) Does REM sleep contribute to subjective wake time in primary insomnia? A comparison of polysomnographic and subjective sleep in 100 patients. J Sleep Res 17(2):180–190

    Article  PubMed  Google Scholar 

  22. Parrino L, Boselli M, Bucino G, Spaggiari M, Di Giovani G, Terzano M (1996) The cyclic alternating pattern plays a gate-control on periodic limb movements during non-rapid eye movement sleep. J Clin Neurophys 13(4):314–323

    Article  CAS  Google Scholar 

  23. Farina B, Marca G, Grochocinske V, Mazza M, Buysee D, Giannantonio M, Mennuni G, Risio S, Kupfer D, Frank E (2003) Microstructure of sleep in depressed patients according to the cyclic alternating pattern. J Aff Disorders 77:227–235

    Article  Google Scholar 

  24. Rizzi M, Sarzi-Puttini P, Capsoni F, Andreoli A, Pecis M, Colombo S, Carrabba M, Sergi M (2004) Cyclic alternating pattern: a new marker of sleep alteration in patients with fibromyalgia? J Rheumatol 31(6):1193–1199

    PubMed  Google Scholar 

  25. Ramar K, Desrues B, Ramar P, Morgenthaler T (2013) Analysis of cardiopulmonary coupling to assess adaptive servo-ventilation success in complex sleep apnea management. Sleep & Breathing (in press)

  26. Khoo MC, Koh SS, Shin JJ, Westbrook PR, Berry RB (1996) Ventilatory dynamics during transient arousal from NREM sleep: implications for respiratory control stability. J App Physio 80(5):1475–1484

    CAS  Google Scholar 

  27. Abbey N, Cooper K, Kwentus J (1989) Benefit of nasal CPAP in obstructive sleep apnea is due to positive pharyngeal pressure. Sleep 12(5):420–422

    PubMed  CAS  Google Scholar 

  28. Demirozu MC, Chediak AD, Nay KN, Cohn MA (1991) A comparison of nine nasal continuous positive airway pressure machines in maintaining mask pressure during simulated inspiration. Sleep 14(3):259–262

    PubMed  CAS  Google Scholar 

  29. Epstein LJ, Kristo D, Strollo PJ, Friedman N, Malhota A, Patil SP, Ramar K, Rogers R, Schwab R, Weaver E, Weinstein M (2009) Adult Obstructive Sleep Apnea Task Force of the American Academy of Sleep Medicine. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med; 5(3):263–276

    PubMed  Google Scholar 

  30. Kribbs N, Pack A, Kline L, Smith P, Smith P, Schwartz A, Schubert N, Redline S, Henry J, Getsy J, Dinges D (1993) Objective measurement of patterns of nasal CPAP use by patients with obstructive sleep apnea. Am Rev Respir Dis 147:887–895

    Article  PubMed  CAS  Google Scholar 

  31. Reite M, Jackson D, Cahoon RL, Weil JV (1975) Sleep physiology at high altitude. Electroencephalogr Clin Neurophysiol 38(5):463–471

    Article  PubMed  CAS  Google Scholar 

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Conflict of interest

JH received research grant support from Embla for this work. PJS was a consultant for MyCardio, LLC. The study was funded by MyCardio, LLC.

Author information

Authors and Affiliations

  1. Division of Sleep Medicine, National Jewish Health, 1400 Jackson St, Denver, CO, 80206-2762, USA

    John Harrington & Teofilo L. Lee-Chiong Jr

  2. AWP-Freiburg, Immental Str. 11, 79104, Freiburg, Germany

    Preetam J. Schramm

  3. Neurology and Sleep Medicine Carle Physician Group, 602 W. University Ave., Urbana, IL, 61801, USA

    Charles R. Davies

Authors
  1. John Harrington
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  2. Preetam J. Schramm
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  3. Charles R. Davies
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  4. Teofilo L. Lee-Chiong Jr
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Corresponding author

Correspondence to Preetam J. Schramm.

Additional information

Clinical trial registry name

Study Comparing In-laboratory Polysomnography Electrocardiogram (PSG ECG) to Simultaneously Recorded In-laboratory ECG on the CPC M1 Device.

Registration number

NCT01234077.

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Harrington, J., Schramm, P.J., Davies, C.R. et al. An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea. Sleep Breath 17, 1071–1078 (2013). https://doi.org/10.1007/s11325-013-0804-9

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  • DOI: https://doi.org/10.1007/s11325-013-0804-9

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