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Dynamics of snoring sounds and its connection with obstructive sleep apnea

Listed author(s):
  • Alencar, Adriano M.
  • da Silva, Diego Greatti Vaz
  • Oliveira, Carolina Beatriz
  • Vieira, André P.
  • Moriya, Henrique T.
  • Lorenzi-Filho, Geraldo
Registered author(s):

    Snoring is extremely common in the general population and when irregular may indicate the presence of obstructive sleep apnea. We analyze the overnight sequence of wave packets — the snore sound — recorded during full polysomnography in patients referred to the Sleep Laboratory due to suspected obstructive sleep apnea. We hypothesize that irregular snore, with duration in the range between 10 and 100 s, correlates with respiratory obstructive events. We find that the number of irregular snores — easily accessible, and quantified by what we call the snore time interval index (STII) — is in good agreement with the well-known apnea–hypopnea index, which expresses the severity of obstructive sleep apnea and is extracted only from polysomnography. In addition, the Hurst analysis of the snore sound itself, which calculates the fluctuations in the signal as a function of time interval, is used to build a classifier that is able to distinguish between patients with no or mild apnea and patients with moderate or severe apnea.

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    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 392 (2013)
    Issue (Month): 1 ()
    Pages: 271-277

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    Handle: RePEc:eee:phsmap:v:392:y:2013:i:1:p:271-277
    DOI: 10.1016/j.physa.2012.08.008
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    1. Zhang, J. & Yang, X.C. & Luo, L. & Shao, J. & Zhang, C. & Ma, J. & Wang, G.F. & Liu, Y. & Peng, C.-K. & Fang, J., 2009. "Assessing severity of obstructive sleep apnea by fractal dimension sequence analysis of sleep EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(20), pages 4407-4414.
    2. Ishizaki, Ryuji & Shinba, Toshikazu & Mugishima, Go & Haraguchi, Hikaru & Inoue, Masayoshi, 2008. "Time-series analysis of sleep–wake stage of rat EEG using time-dependent pattern entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3145-3154.
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