Volume 29, Issue 1 (March 2025)                   Physiol Pharmacol 2025, 29(1): 96-105 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Abbasi Feijani F, Hajihashemi S, Moini A, Pazhoohan S. Impaired respiratory control system adaptability in patients with COPD: Evidence from complexity analysis of oxygen saturation variability. Physiol Pharmacol 2025; 29 (1) : 10
URL: http://ppj.phypha.ir/article-1-2302-en.html
Impaired respiratory control system adaptability in patients with COPD: Evidence from complexity analysis of oxygen saturation variability
Fatemeh Abbasi Feijani , Saeed Hajihashemi , Abdollatif Moini , Saeed Pazhoohan
Abstract:   (856 Views)

Introduction: People with chronic obstructive pulmonary disease (COPD) often experience exacerbations and impaired gas exchange, leading to hypoxemia that may require hospitalization. Pattern analysis of capillary oxygen saturation (SpO2) variability can provide valuable insight into the adaptive capacity of the respiratory control system under this condition. Therefore, this study tested the hypothesis that the adaptability of the respiratory control system is reduced in patients with COPD.

Methods: In this study, we utilized entropy and fractal-like correlation properties of SpO2 time series in patients with COPD. We analyzed pulse oximetry data from 13 patients with COPD during hospitalization and discharge time and compared them to 16 age- and sex-matched control subjects. SpO2 variability analysis of a 25-minute time series was performed using sample entropy, multiscale entropy, and detrended fluctuation analysis.

Results: Entropy analysis revealed a complex pattern of SpO2 variability in healthy controls and patients with COPD. Both short-term (α1) and long-term fractal-like exponent (α2) were higher in patients with COPD compared to healthy controls. SpO2 entropy and mean were significantly lower in patients with COPD in comparison with controls. There was no statistically significant difference in SpO2 complexity measures between the hospitalization and discharge phases in these patients.

Conclusion: The respiratory control system in patients with COPD exhibits less complexity and information processing. These non-invasive analytical methods have the potential for future clinical application to monitor the integrity of respiratory control in individuals suffering from chronic respiratory diseases.

Article number: 10
Keywords: oxygen saturation variability, pulse oximetry, entropy, COPD, hypoxia
Full-Text [PDF 632 kb]   (120 Downloads)    
Type of Manuscript: Clinical research article | Subject: Respiratory Physiology/Pharmacology
References
1. Adeloye D, Song P, Zhu Y, Campbell H, Sheikh A, Rudan I. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. Lancet Respir Med 2022; 10: 447-458.
3. Al Rajeh A, Bhogal A S, Zhang Y, Costello J T, Hurst J R, Mani A R. Application of oxygen saturation variability analysis for the detection of exacerbation in individuals with COPD: A proof-of-concept study. Physiol Rep 2021; 9: e15132. [DOI:10.14814/phy2.15132]
4. Alassafi M O, Aziz W, AlGhamdi R, Alshdadi A A, Nadeem M S A, Khan I R, et al. Complexity reduction of oxygen saturation variability signals in COVID-19 patients: Implications for cardiorespiratory control. J Infect Public Health 2024; 17: 601-608.
6. Amalakanti S, Pentakota M R. Pulse oximetry overestimates oxygen saturation in COPD. Respir Care 2016; 61: 423-7. [DOI:10.4187/respcare.04435]
7. Bhogal A S, Mani A R. Pattern Analysis of oxygen saturation variability in healthy individuals: entropy of pulse oximetry signals carries information about mean oxygen saturation. Front Physiol 2017; 8: 555. [DOI:10.3389/fphys.2017.00555]
8. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of biological signals. Phys Rev E Stat Nonlin Soft Matter Phys 2005; 71: 021906. [DOI:10.1103/PhysRevE.71.021906]
9. Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 2002; 89: 068102. [DOI:10.1103/PhysRevLett.89.068102]
10. Costello J T, Bhogal A S, Williams T B, Bekoe R, Sabir A, Tipton M J, et al. Effects of normobaric hypoxia on oxygen saturation variability. High Alt Med Biol 2020; 21: 76-83.
12. Fleetham J A, Mezon B, West P, Bradley C A, Anthonisen N R, Kryger M H. Chemical control of ventilation and sleep arterial oxygen desaturation in patients with COPD. Am Rev Respir Dis 1980; 122: 583-9.
13. Frey U, Maksym G, Suki B. Temporal complexity in clinical manifestations of lung disease. J Appl Physiol (1985) 2011; 110: 1723-31. [DOI:10.1152/japplphysiol.01297.2010]
14. Gheorghita M, Wikner M, Cawthorn A, Oyelade T, Nemeth K, Rockenschaub P, et al. Reduced oxygen saturation entropy is associated with poor prognosis in critically ill patients with sepsis. Physiol Rep 2022; 10: e15546. [DOI:10.14814/phy2.15546]
15. Gholami M, Mazaheri P, Mohamadi A, Dehpour T, Safari F, Hajizadeh S, et al. Endotoxemia is associated with partial uncoupling of cardiac pacemaker from cholinergic neural control in rats. Shock 2012; 37: 219-27. [DOI:10.1097/SHK.0b013e318240b4be]
16. Goldberger A L, Rigney D R, West B J. Chaos and fractals in human physiology. Sci Am 1990; 262: 42-9. [DOI:10.1038/scientificamerican0290-42]
17. Jiang Y, Costello J T, Williams T B, Panyapiean N, Bhogal A S, Tipton M J, et al. A network physiology approach to oxygen saturation variability during normobaric hypoxia. Exp Physiol 2021; 106: 151-159. [DOI:10.1113/EP088755]
18. Jubran A. Pulse oximetry. Crit Care 2015; 19: 272. [DOI:10.1186/s13054-015-0984-8]
19. Kent B D, Mitchell P D, McNicholas W T. Hypoxemia in patients with COPD: cause, effects, and disease progression. Int J Chron Obstruct Pulmon Dis 2011; 6: 199-208. [DOI:10.2147/COPD.S10611]
20. Lane D J, Howell J B. Relationship between sensitivity to carbon dioxide and clinical features in patients with chronic airways obstruction. Thorax 1970; 25: 150-9.
22. Lefebvre J H, Goodings D A, Kamath M V, Fallen E L. Predictability of normal heart rhythms and deterministic chaos. Chaos 1993; 3: 267-276. [DOI:10.1063/1.165990]
23. Peng C K, Havlin S, Stanley H E, Goldberger A L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995; 5: 82-7.
25. Pincus S M. Greater signal regularity may indicate increased system isolation. Math Biosci 1994; 122: 161-81. [DOI:10.1016/0025-5564(94)90056-6]
26. Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 2000; 278: H2039-49.
28. Ritchie A I, Baker J R, Parekh T M, Allinson J P, Bhatt S P, Donnelly L E, et al. Update in Chronic Obstructive Pulmonary Disease 2020. Am J Respir Crit Care Med 2021; 204: 14-22.
30. Satti R, Abid N U, Bottaro M, De Rui M, Garrido M, Raoufy M R, et al. The Application of the Extended Poincaré Plot in the Analysis of Physiological Variabilities. Front Physiol 2019; 10: 116. [DOI:10.3389/fphys.2019.00669]
31. Shirazi A H, Raoufy M R, Ebadi H, De Rui M, Schiff S, Mazloom R, et al. Quantifying memory in complex physiological time-series. PLoS One 2013; 8: e72854.
33. Sugihara G, May R M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 1990; 344: 734-41.
35. Thamrin C, Nydegger R, Stern G, Chanez P, Wenzel S E, Watt R A, et al. Associations between fluctuations in lung function and asthma control in two populations with differing asthma severity. Thorax 2011; 66: 1036-42. [DOI:10.1136/thx.2010.156489]
36. Tsai C H, Ma H P, Lin Y T, Hung C S, Hsieh M C, Chang T Y, et al. Heart Rhythm Complexity Impairment in Patients with Pulmonary Hypertension. Sci Rep 2019; 9: 10710.
38. Tsai C H, Ma H P, Lin Y T, Hung C S, Huang S H, Chuang B L, et al. Usefulness of heart rhythm complexity in heart failure detection and diagnosis. Sci Rep 2020; 10: 14916.
40. Vaquerizo-Villar F, Álvarez D, Kheirandish-Gozal L, Gutiérrez-Tobal G C, Barroso-García V, Crespo A, et al. Detrended fluctuation analysis of the oximetry signal to assist in aediatric sleep apnoea-hypopnoea syndrome diagnosis. Physiol Meas 2018; 39: 114006. [DOI:10.1088/1361-6579/aae66a]
41. WHO. Chronic obstructive pulmonary disease. Accessed 16 March 2023: https://wwwwhoint/zh/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(COPD).
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.