Advertisement

ApEn for assessing hypoxemia severity in obstructive sleep apnea hypopnea syndrome patients

  • 21 Accesses

Abstract

Objective

A new index, approximate entropy (ApEn) of oxygen saturation, was used to assess the severity of hypoxemia in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), determine the correlation with other parameters, and explore its clinical value.

Methods

A retrospective analysis was performed on 1200 patients with OSAHS and snorers (normal control). All subjects underwent sleep apnea monitoring for 6 h. Subjects were divided into four subgroups by apnea-hypopnea index (AHI): normal control (AHI < 5), mild OSAHS (5 ≤ AHI < 15), moderate OSAHS (15 ≤ AHI < 30), and severe OSAHS 104 (AHI ≥ 30). ApEn was initially compared among the subgroups. Then a correlation analysis of AHI with ApEn and a correlation analysis of ApEn with oxygen desaturation index (ODI), lowest oxygen saturation (LO2), and T < 90% were performed. (2) The AHI was used as the gold standard, and an attempt was performed to determine the value of ApEn to assess the severity of hypoxemia in OSAHS.

Results

Among the 1200 subjects, 822 subjects were men (72%) and mean age was 53.2 ± 15.2 years (range 24–95 years). The ApEn in each group was significantly different (P <0.001), and the ApEn synchronously increased with AHI. Furthermore, a significant difference in ApEn was found among the groups (P <0.001). In addition, ApEn had a good correlation with ODI, LO2, and T <90%. According to the ROC analysis results, the boundary value of ApEn to judge OSAHS patients with mild, moderate, and severe hypoxia was 16.72, 17.84, and 20.06, respectively.

Conclusion

ApEn synchronously increased with the AHI and had a good correlation with AHI, ODI, LO2, and T <90%. These findings suggest that ApEn may have clinical value for assessing hypoxia severity in OSAHS patients.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1

References

  1. 1.

    Pincus S (1995) Approximate entropy (Apen) as a complexity measure. Chaos. 5:110–117

  2. 2.

    Pincus S, Huang W (1992) Approximate entropy-statistical properties and applications. Commun Stat Theory Methods 21:3061–3077

  3. 3.

    Stergiou N, Harbourne R, Cavanaugh J (2006) Optimal movement variability: a new theoretical perspective for neurologic physical therapy. J Neurol Phys Ther 30:120–129

  4. 4.

    Faure P, Korn H (2011) Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation. C.R. Acad. Sci. III. 324:773–793

  5. 5.

    Wolf A, Swift JB, Swinney HL, Vastano JA (1985) Determining Lyapunov exponents from a time-series. Physica D 16:285–317

  6. 6.

    Chesson AL Jr, Willam M (1993) Assessment of hypoxemia in patients with sleep disorders using saturation impairment time. Am Rev Respir Dis 148:1592–1598

  7. 7.

    Kirby SC, Anderson WM, Chesson AL, George RB (1992) Computer quantitation of saturation impairment time as an index of oxygenation during sleep. Comput Methods Prog Biomed 38:107–115

  8. 8.

    Mutlu LC, Tülübaş F, Alp R, Kaplan G, Yildiz ZD, Gürel A (2017) Serum YKL-40 level is correlated with apnea hypopnea index in patients with obstructive sleep apnea syndrome. Eur Rev Med Pharmacol Sci 21:4161–4166

  9. 9.

    Sozer V, Kutnu M, Atahan E, Calıskaner Ozturk B, Hysi E, Cabuk C, Musellim B, Simsek G, Uzun H (2018) Changes in inflammatory mediators as a result of intermittent hypoxia in obstructive sleep apnea syndrome. Clin Respir J 12:1615–1622

  10. 10.

    Rusu A, Bala CG, Craciun AE, Roman G (2017) HbA1c levels are associated with severity of hypoxemia and not with apnea hypopnea index in patients with type 2 diabetes: results from a cross-sectional study. J Diabetes 9:555–561

  11. 11.

    Song YJ, Kwon JH, Kim JY, Kim BY, Cho KI (2016) The platelet-to-lymphocyte ratio reflects the severity of obstructive sleep apnea syndrome and concurrent hypertension. Clin Hypertens 22:1

  12. 12.

    Oyama J, Nagatomo D, Yoshioka G, Yamasaki A, Kodama K, Sato M, Komoda H, Nishikido T, Shiraki A, Node K (2016) The relationship between neutrophil to lymphocyte ratio, endothelial function, and severity in patients with obstructive sleep apnea. J Cardiol 67:295–302

  13. 13.

    Ozanturk E, Ucar ZZ, Varol Y, Koca H, Demir AU, Kalenci D, Halilcolar H, Ozacar R (2006) Urinary uric acid excretion as an indicator of severe hypoxia and mortality in patients with obstructive sleep apnea and chronic obstructive pulmonary disease. Rev Port Pneumol 22:18–26

  14. 14.

    Pincus SM, Keefe DL (1992) Quantification of hormone pulsatility via an approximate entropy algorithm. Am J Physiol Endocrinol Metab 262:741–754

  15. 15.

    Pincus SM (2001) Assessing serial irregularity and its implications for health. Ann N Y Acad Sci 954:245–267

  16. 16.

    Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A 88:2297–2301

  17. 17.

    Guo XY, Li FJ (2017) Chaotic features on excess/deficiency syndrome of chronic stable angina pectoris based on analyzing approximate entropy of heart rate variability. Zhongguo Zhong Xi Yi Jie He Za Zhi 37:297–301

  18. 18.

    Byun S, Kim AY, Jang EH, Kim S, Choi KW, Yu HY, Jeon HJ (2017) Entropy analysis of heart rate variability and its application to recognize major depressive disorder: a pilot study. Comput Biol Med. 80:137–147

  19. 19.

    Skoric T, Mohamoud O, Milovanovic B, Japundzic-Zigon N, Bajic D (2017) Binarized cross-approximate entropy in crowdsensing environment. Comput Biol Med 80:137–147

  20. 20.

    Tsuji Y, Suzuki N, Hitomi Y, Yoshida T, Mizuno-Matsumoto Y (2017) Quantification of autonomic nervous activity by heart rate variability and approximate entropy in high ultrafiltration rate during hemodialysis. Clin Exp Nephrol 21:524–530

  21. 21.

    Singh V, Gupta A, Sohal JS, Singh A (2019) A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of age-stratified subjects. Med Biol Eng Comput 57:741–755

  22. 22.

    Caldirola D, Bellodi L, Caumo A, Migliarese G, Perna G (2004) Approximate entropy of respiratory patterns in panic disorder. Am J Psychiatry 161:79–87

  23. 23.

    Bastos de Figueiredo JC, Diambra L, Glass L, Malta CP (2002) Chaos in two-loop negative feedback systems. Phys Rev E Stat Nonlin Soft Matter Phys 65:051905

  24. 24.

    Sharmila A, Aman Raj S, Shashank P, Mahalakshmi P (2018) Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study. J Med Eng Technol 42:1–8

  25. 25.

    Burioka N, Cornélissen G, Halberg F, Kaplan DT, Suyama H, Sako T, Shimizu E (2003) Approximate entropy of human respiratory movement during eye-closed waking and different sleep stages. Chest 123:80–86

  26. 26.

    Caldirola D, Bellodi L, Caumo A, Migliarese G, Perna G (2004) Approximate entropy of respiratory patterns in panic disorder. Am J Psychiatry 161:79–87

  27. 27.

    Levy P, Pépin JL, Deschaux-Blanc C, Paramelle B, Brambillaet C (1996) Accuracy of oximetry for detection of respiratory disturbances in sleep apnea syndrome. Chest 109:395–399

Download references

Author information

Correspondence to Songbai Lin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Peking Union Medical College Hospital committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Huang, R., Xiao, Y. et al. ApEn for assessing hypoxemia severity in obstructive sleep apnea hypopnea syndrome patients. Sleep Breath (2020). https://doi.org/10.1007/s11325-019-02004-0

Download citation

Keywords

  • Obstructive sleep apnea hypopnea syndrome
  • AHI
  • ApEn