Medical & Biological Engineering & Computing

, Volume 50, Issue 12, pp 1249–1259 | Cite as

Fluctuation analysis of respiratory impedance waveform in asthmatic patients: effect of airway obstruction

  • J. Veiga
  • A. J. Lopes
  • J. M. Jansen
  • P. L. Melo
Original Article


Fluctuation analysis has great potential to contribute to pulmonary clinical science and practice. We evaluated the relationship between asthma and the respiratory impedance recurrence period density entropy (RPDEnZrs) and the variability (SDZrs). A non-invasive and simple protocol for assessing respiratory mechanics during spontaneous breathing was used in a group of 74 subjects with various levels of airway obstruction. Airway obstruction resulted in a reduction in the RPDEnZrs that was significantly correlated with both spirometric indices of airway obstruction (R = 0.48, p < 0.0001) and mean respiratory impedance (R = −0.83, p < 0.0001). These results suggest that the impedance pattern becomes less complex in asthmatic patients, which may explain the reduction in respiratory systems’ adaptability to daily life activities. Preliminary evaluations indicate that RPDEnZrs may contribute to the asthma diagnosis, presenting accuracies of 82 and 87 % in patients with moderate and severe airway obstruction, respectively. On the other hand, SDZrs increased with obstruction (p < 0.0001) and was inversely correlated with spirometric indices of obstruction (R = −0.42, p = 0.0003) and directly associated with mean impedance (R = 0.88, p < 0.0001). This analysis contributes to elucidate previous studies and identified respiratory changes in patients with moderate and severe obstruction with an adequate accuracy (85 and 87 %, respectively).


Fluctuation analysis Approximate entropy Recurrence period density entropy Variability Respiratory mechanics Complexity Asthma 



We would like to thank the anonymous reviewer for constructive comments and propositions. We also wish to thank Alvaro C. D. Faria for assistance with the statistical tests and Guilherme P. Esteves for technical assistance. This study was supported by the Brazilian Council for Scientific and Technological Development (CNPq) and Rio de Janeiro State Research Supporting Foundation (FAPERJ).


  1. 1.
    Boser SR, Park H, Perry SF, Menache MG, Green FH (2005) Fractal geometry of airway remodeling in human asthma. Am J Respir Crit Care Med 172:817–823PubMedCrossRefGoogle Scholar
  2. 2.
    Caldirola D, Bellodi L, Caumo A, Migliarese G, Perna G (2004) Approximate entropy of respiratory patterns in panic disorder. Am J Psychiatry 161(1):79–87PubMedCrossRefGoogle Scholar
  3. 3.
    Diba C, Salome CM, Reddel HK, Thorpe CW, Toelle B, King GG (2007) Short-term variability of airway caliber: a marker of asthma? J Appl Physiol 103:296–304PubMedCrossRefGoogle Scholar
  4. 4.
    Dragomir A, Akay Y, Curran AK, Akay M (2008) Complexity measures of the central respiratory networks during wakefulness and sleep. J Neural Eng 5:254–261PubMedCrossRefGoogle Scholar
  5. 5.
    Akay M (2005) Influence of peripheral chemodenervation on the complexity of respiratory patterns during early maturation. Med Biol Eng Comput 43:793–799PubMedCrossRefGoogle Scholar
  6. 6.
    Frey U, Brodbeck T, Majumdar A, Taylor DR, Town GI, Silverman M, Suki B (2005) Risk of severe asthma episodes predicted from fluctuation analysis of airway function. Nature 438:667–670PubMedCrossRefGoogle Scholar
  7. 7.
    Frey U, Maksym GN, Suki B (2011) Temporal complexity in clinical manifestations of lung disease. J Appl Physiol 110(6):1723–1731PubMedCrossRefGoogle Scholar
  8. 8.
    Fusheng Y, Bo H, Qingyu T (2001) Approximate entropy and its application in biosignal analysis. In: Akay M (ed) Nonlinear biomedical signal processing, vol 2., Dynamic analysis and modelingIEEE Press, New Jersey, pp 72–91Google Scholar
  9. 9.
    Glenny RW (2011) Emergence of matched airway and vascular trees from fractal rules. J Appl Physiol 110(4):1119–1129PubMedCrossRefGoogle Scholar
  10. 10.
    Kaminsky DA, Irvin CG, Sterk PJ (2011) Complex Systems in Pulmonary Medicine: a systems biology approach to lung disease. J Appl Physiol 110(6):1716–1722PubMedCrossRefGoogle Scholar
  11. 11.
    Lall CA, Cheng N, Hernandez P, Pianosi PT, Dali Z, Abouzied A, Maksym GN (2007) Airway resistance variability and response to bronchodilator in children with asthma. Eur Respir J 30:260–268PubMedCrossRefGoogle Scholar
  12. 12.
    Leary D, Bhatawadekar SA, Parraga G, Maksym GN (2012) Modeling stochastic and spatial heterogeneity in a human airway tree to determine variation in respiratory system resistance. J Appl Physiol 112:167–175PubMedCrossRefGoogle Scholar
  13. 13.
    Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM (2007) Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed Eng Online 6:23PubMedCrossRefGoogle Scholar
  14. 14.
    Macklem PT (2005) Complexity and respiration: a matter of life and death. In: Hamid Q, Shannon J, Martin J (eds) Physiologic basis of respiratory disease. BC Decker, Hamilton, pp 605–609Google Scholar
  15. 15.
    Macklem PT, Seely A (2010) Towards a definition of life. Perspect Biol Med 53:330–340PubMedCrossRefGoogle Scholar
  16. 16.
    Melo PL, Lemes LNA (2002) Instrumentation for the analysis of respiratory system disorders during sleep: design and application. Rev Sci Instrum 73:3926–3932CrossRefGoogle Scholar
  17. 17.
    Muskulus M, Slats AM, Sterk PJ, Verduyn-Lunel S (2010) Fluctuations and determinism of respiratory impedance in asthma and chronic obstructive pulmonary disease. J Appl Physiol 109(6):1582–1591PubMedCrossRefGoogle Scholar
  18. 18.
    Oostveen E, MacLeod D, Lorino H, Farre R, Hantos Z, Desager K, Marchal F (2003) The forced oscillation technique in clinical practice: methodology, recommendations and future developments. Eur Respir J 22:1026–1041PubMedCrossRefGoogle Scholar
  19. 19.
    Papaioannou VE, Chouvarda IG, Maglaveras NK, Pneumatikos IA (2011) Study of multiparameter respiratory pattern complexity in surgical critically ill patients during weaning trials. BMC Physiol 21(11):2CrossRefGoogle Scholar
  20. 20.
    Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88(6):2297–2301PubMedCrossRefGoogle Scholar
  21. 21.
    Pincus SM, Huang W (1992) Approximate entropy: statistical properties and applications. Commun Stat Theory Methods 21(11):3061–3077CrossRefGoogle Scholar
  22. 22.
    Pincus SM, Goldberger AL (1994) Physiological time series analysis: what does regularity quantify. Am J Physiol 266:H1643–H1656PubMedGoogle Scholar
  23. 23.
    Pincus SM (1994) Greater signal regularity may indicate increased system isolation. Math Biosci 122(161–181):1994Google Scholar
  24. 24.
    Que CL, Kenyon CM, Olivenstein R, Macklem PT, Maksym GN (2001) Homeokinesis and short-term variability of human airway caliber. J Appl Physiol 91:1131–1141PubMedGoogle Scholar
  25. 25.
    Reddy C (2009) Bronchoprovocation testing. Clinic Rev Allerg Immunol 37:167–172CrossRefGoogle Scholar
  26. 26.
    Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049PubMedGoogle Scholar
  27. 27.
    Suki B (2002) Fluctuations and power laws in pulmonary physiology. Am J Respir Crit Care Med 166:133–137PubMedCrossRefGoogle Scholar
  28. 28.
    Suki B (2010) In search of complexity. J Appl Physiol 109(1571–1572):2010. doi: 10.1152/japplphysiol.01102.2010 Google Scholar
  29. 29.
    Suki B, Bates JHT, Frey U (2011) Complexity and emergent phenomena. Compr Physiol 1:995–1029Google Scholar
  30. 30.
    Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293PubMedCrossRefGoogle Scholar
  31. 31.
    Thamrin C, Stern G (2010) New methods: what do they tell us? Fluctuation analysis of lung function. Eur Respir Mon 47:310–324CrossRefGoogle Scholar
  32. 32.
    Veiga J, Lopes AJ, Jansen JM, Melo PL (2009) Within-breath analysis of respiratory mechanics in asthmatic patients by forced oscillation. Clinics (São Paulo) 64(7):649–656CrossRefGoogle Scholar
  33. 33.
    Veiga J, Lopes AJ, Jansen JM, Melo PL (2011) Airflow pattern complexity and airway obstruction in asthma. J Appl Physiol 111(2):412–419PubMedCrossRefGoogle Scholar
  34. 34.
    Witten IH, Frank E (1999) Data mining. Morgan Kaufmann, San FranciscoGoogle Scholar
  35. 35.
    World Health Organization (2011) GINA: Global Initiative for Asthma (Online). Accessed 24 Sept 2012

Copyright information

© International Federation for Medical and Biological Engineering 2012

Authors and Affiliations

  • J. Veiga
    • 1
  • A. J. Lopes
    • 2
  • J. M. Jansen
    • 2
  • P. L. Melo
    • 1
    • 3
  1. 1.Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of EngineeringState University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.Pulmonary Function Laboratory, Faculty of Medical SciencesState University of Rio de JaneiroRio de JaneiroBrazil
  3. 3.Clinical and Experimental Research Laboratory in Vascular Biology, Institute of BiologyState University of Rio de JaneiroRio de JaneiroBrazil

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