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Breathing irregularity during wakefulness associates with CPAP acceptance in sleep apnea

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Abstract

Purpose

Individuals have different breathing patterns at rest, during wakefulness, and during sleep, and patients with sleep apnea are no different. The hypothesis for this study was that breathing irregularity during wakefulness associates with CPAP acceptance in obstructive sleep apnea (OSA).

Methods

From a 2007–2010-database of patients with a diagnostic polysomnography (PSG) and prescribed CPAP (n = 380), retrospectively, 66 patients who quit CPAP treatment at 6 months were identified. Among them, 27 OSA patients quit despite having no side effects for discontinuing CPAP (Group A) and were compared to a matched group (age, body mass index, and apnea–hypopnea index) with good 6-month CPAP adherence (Group B; n = 21). Five minutes of respiratory signal during wakefulness at the initial PSG were extracted from respiratory inductance plethysmography recordings, and measured in a blinded fashion. The coefficients of variation (CV) for the breath-to-breath inspiration time (T i), expiration time (T e), T i + T e (T tot), and relative tidal volume, as well as an independent information theory-based metric of signal pattern variability (mutual information) were compared between groups.

Results

The CV for tidal volume was significantly greater (p = 0.001), and mutual information was significantly lower (p = 0.041) in Group A as compared to Group B.

Conclusions

Differences in two independent measures of breathing irregularity correlated with CPAP rejection in OSA patients without nasal symptoms or comorbidity. Prospective studies of adherence should examine traits of breathing stability.

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Acknowledgments

The authors would like to thank Kaoru Senzaki, RPSGT, for her help with polysomnogram scoring. This study is partly supported by a Grant-in-Aid for Young Scientists (B) (21790781) from The Ministry of Education, Culture, Sports, Science and Technology, Japan. US investigators were supported in part by the NIH-NHLBI [R33HL087340-01], and the VA Research Service.

Conflicts of interest

None of the authors have financial conflicts of interest to declare as it relates to the contents of this manuscript.

Author information

Authors and Affiliations

  1. Second Department of Internal Medicine (Department of Respiratory Medicine), Nara Medical University, 840 Shijo-cho, Kashihara, Nara, 634-8522, Japan

    Motoo Yamauchi, Yukio Fujita, Masanori Yoshikawa & Hiroshi Kimura

  2. Division of Pulmonary, Critical Care and Sleep Medicine, Case Western Reserve University, Cleveland, OH, USA

    Frank J. Jacono, Cara K. Campanaro & Kingman P. Strohl

  3. Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA

    Frank J. Jacono, Cara K. Campanaro & Kingman P. Strohl

  4. Department of Internal Medicine, Tenri City Hospital, Tenri, Japan

    Yoshinobu Ohnishi

  5. Department of Pulmonology, Fukuoka National Hospital, Fukuoka, Japan

    Hiroshi Nakano

  6. Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA

    Kenneth A. Loparo

Authors
  1. Motoo Yamauchi
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  2. Frank J. Jacono
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  3. Yukio Fujita
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  4. Masanori Yoshikawa
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  5. Yoshinobu Ohnishi
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  7. Cara K. Campanaro
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  9. Kingman P. Strohl
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  10. Hiroshi Kimura
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Corresponding author

Correspondence to Motoo Yamauchi.

Appendix

Appendix

Mutual Information: Mutual information (MI) is a measure of the statistical dependence between two time series, or two collections of points from a data set, that can arise from both linear and nonlinear sources [42]. The Mutual information between a given time series x(t) and its time-shifted version x(t + τ) is computed from the joint probability distribution of x(t) and x(t + τ), where τ represents a time lag. The joint probability distribution is defined as P[x(t), x(t + τ)], where P[x(t)] and P[x(t + τ)] are the marginal distributions of the original and time-shifted time series, respectively. The MI can be computed as follows:

$$ MI\left[ {x(t),\,x\left( {t+\tau } \right)} \right]=\sum\limits_i {\sum\limits_j {P\left[ {x_i (t),\,{x_j}\left( {t+\tau } \right)} \right]\log \left[ {\frac{{P\left[ {x_i (t),\,{x_j}\left( {t+\tau } \right)} \right]}}{{P\left[ {x_i (t)} \right]\cdot P\left[ {x_j \left( {t+\tau } \right)} \right]}}} \right]} } $$

Because the breathing pattern over long time periods is strongly periodic, we computed MI for τ values from one sample (adjacent points separated by 100 ms) to one cycle length. MI tends to decrease quickly as τ is increased from a lag of one and then becomes more uniform at higher time lags, and the average MI of a given epoch was quantified excluding small lags as defined by the first minimum of the MI function.

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Yamauchi, M., Jacono, F.J., Fujita, Y. et al. Breathing irregularity during wakefulness associates with CPAP acceptance in sleep apnea. Sleep Breath 17, 845–852 (2013). https://doi.org/10.1007/s11325-012-0775-2

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  • DOI: https://doi.org/10.1007/s11325-012-0775-2

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