Skip to main content
SpringerLink
Log in

The augmented RIC model of the human respiratory system

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

This paper describes the augmented RIC model of respiratory impedance and analyzes its parameter values estimated—by a modified Newton method with least squares criterion—from impulse oscillometry data. The data were from asthmatic children, tested pre- and post-bronchodilator, and from healthy adults and a second group of adults with COPD. Our analyses show that the augmented RIC model was 13.7–66.6% more accurate than the extended RIC model at fitting these data, while its parameter estimates were within previously reported ranges, unlike the Mead1969, DuBois and Mead models, which typically yielded compliance estimates exceeding 200 l/kPa. Additionally, the augmented RIC model’s C p parameter, representing peripheral airway compliance, is a statistically significant discriminator between unconstricted and constricted conditions (with p < 0.001) occurring in asthma and COPD. This corresponds well with current medical understanding, so the augmented RIC model is potentially useful for detection and treatment of airflow obstruction.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Adby PR, Dempster MAH (1974) Introduction to optimization methods. Chapman and Hall, London, UK

    MATH  Google Scholar 

  2. Collins TP, Tabor GR, Young PG (2007) A computational fluid dynamics study of inspiratory flow in orotracheal geometries. Med Biol Eng Comput 45(9):829–836. doi:10.1007/s11517-007-0238-2

    Article  Google Scholar 

  3. Diong B, Nazeran H, Nava P, Goldman M (2007) Modeling human respiratory impedance. IEEE Eng Med Biol Soc Mag Spec Issue Respir Sound Anal 26:48–55

    Google Scholar 

  4. DuBois AB, Brody AW, Lewis DH, Burgess BF (1956) Oscillation mechanics of lungs and chest in man. J Appl Physiol 8:587–594

    Google Scholar 

  5. Eyles JG, Pimmel RL (1981) Estimating respiratory mechanical parameters in parallel compartment models. IEEE Trans Biomed Eng BME 28(4):313–317. doi:10.1109/TBME.1981.324798

    Article  Google Scholar 

  6. Eyles JG, Pimmel RL, Fullton JM, Bromberg PA (1982) Parameter estimates in a five-element respiratory mechanical model. IEEE Trans Biomed Eng BME 29(6):460–463. doi:10.1109/TBME.1982.324974

    Article  Google Scholar 

  7. Frei J, Jutla J, Kramer G, Hatzakis GE, Ducharme FM, Davis GM (2005) Impulse oscillometry—reference values in children 100–150 cm in height and 3–10 years of age. Chest 128(3):1266–1273. doi:10.1378/chest.128.3.1266

    Article  Google Scholar 

  8. Grimby G, Takishima T, Graham W, Macklem P, Mead J (1968) Frequency dependence of flow resistance in patients with obstructive lung disease. J Clin Invest 47:1455–1465

    Google Scholar 

  9. Hsia TC (1977) System identification. Lexington Books, Lexington, MA

    Google Scholar 

  10. Jandre FC, Modesto FC, Carvalho AR, Giannella-Neto A (2008) The endotracheal tube biases the estimates of pulmonary recruitment and overdistension. Med Biol Eng Comput 46(1):69–73. doi:10.1007/s11517-007-0227-5

    Article  Google Scholar 

  11. Kjeldgaard J, Hyde R, Speers D, Reichert W (1976) Frequency dependence of total respiratory resistance in early airway disease. Am Rev Respir Dis 144:501–508

    Google Scholar 

  12. Lutchen KR, Costa KD (1990) Physiological interpretations based on lumped element models fit to respiratory impedance data: use of forward–inverse modeling. IEEE Trans Biomed Eng 37(11):1076–1086. doi:10.1109/10.61033

    Article  Google Scholar 

  13. MacLeod D, Birch M (2001) Respiratory input impedance measurements: forced oscillation methods. Med Biol Eng Comput 39(5):505–516. doi:10.1007/BF02345140

    Article  Google Scholar 

  14. Marotta A, Klinnert M, Price M, Larsen G, Liu A (2003) Impulse oscillometry provides an effective measure of lung dysfunction in 4-year-old children at risk for persistent asthma. J Allergy Clin Immunol 112:317–322. doi:10.1067/mai.2003.1627

    Article  Google Scholar 

  15. Martin RJ (2002) Therapeutic significance of distal airway inflammation in asthma. J Allergy Clin Immunol 109(2):S447–S460. doi:10.1067/mai.2002.121409

    Article  Google Scholar 

  16. Mead J (1969) Contribution of compliance of airways to frequency-dependent behavior of lungs. J Appl Physiol 26(5):670–673

    Google Scholar 

  17. Oostveen E, MacLeod D, Lorino H, Farre R, Hantos Z et al (2003) The forced oscillation technique in clinical practice: methodology, recommendations and future developments. Eur Respir J 22:1026–1041. doi:10.1183/09031936.03.00089403

    Article  Google Scholar 

  18. Ortiz G, Menendez R (2002) The effects of inhaled albuterol and salmeterol in 2- to 5- year-old asthmatic children as measured by Impulse Oscillometry. J Asthma 39(6):531–536. doi:10.1081/JAS-120004923

    Article  Google Scholar 

  19. Otis A, McKerrow C, Bartlett R, Mead J, McIlroy M et al (1956) Mechanical factors in distribution of pulmonary ventilation. J Appl Physiol 8:427–443

    Google Scholar 

  20. Rajagiri A, Diong B, Nazeran H, Goldman M (2005) Evaluation of augmented RIC model of adult respiratory impedance based on parameter estimates from Impulse Oscillometry data. In: Proceedings of 12th international conference on biomedical engineering

  21. Rajagiri A, Diong B, Goldman M, Nazeran H (2006) Can the estimated parameter values of the augmented RIC model be used to detect asthma in children? In: Proceedings of IEEE engineering in medicine and biology society conference, pp. 5595–5598

  22. Schmidt M, Foitzik B, Hochmuth O, Schmalisch G (1998) Computer simulation of the measured respiratory impedance in newborn infants and the effect of the measurement equipment. Med Eng Phys 20:220–228. doi:10.1016/S1350-4533(98)00006-X

    Article  Google Scholar 

  23. Sinha NK, Kuszta B (1983) Modeling and identification of dynamic systems. Van Nostrand Reinhold Co, New York

    Google Scholar 

  24. Smith HJ, Reinhold P, Goldman MD (2005) Forced oscillation technique and impulse oscillometry. Eur Respir Mon 31:72–105

    Google Scholar 

  25. Suki B, Bates JH (1991) A nonlinear viscoelastic model of lung tissue mechanics. J Appl Physiol 71(3):826–833

    Google Scholar 

  26. Vogel J, Smidt U (1994) Impulse oscillometry. pmi Verlagsgruppe GmbH. Frankfurt am Main, Germany

    Google Scholar 

  27. Woo T, Diong B, Mansfield L, Goldman M, Nava P, Nazeran H (2004) A comparison of various respiratory system models based on parameter estimates from Impulse Oscillometry data. In: Proceedings of IEEE engineering in medicine and biology society conference, pp 3828–3831

  28. VIASYS MasterScreen IOS. VIASYS/Jaeger, Yorba Linda CA, USA

  29. Yamashiro SM (2007) Non-linear dynamics of human periodic breathing and implications for sleep apnea therapy. Med Biol Eng Comput 45(4):345–356. doi:10.1007/s11517-006-0153-y

    Article  Google Scholar 

Download references

Acknowledgments

This project was supported by grant number S11 ES013339 from the US National Institute of Environmental Health Sciences (NIEHS), NIH. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS, NIH. The help of Dr. Roger Menendez, Allergy & Asthma Center of El Paso, TX, with the children’s IOS data is also gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Engineering, Texas Christian University, Fort Worth, TX, 76129, USA

    Bill Diong & A. Rajagiri

  2. Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX, 79968, USA

    M. Goldman & H. Nazeran

Authors
  1. Bill Diong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Rajagiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Goldman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. H. Nazeran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bill Diong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Diong, B., Rajagiri, A., Goldman, M. et al. The augmented RIC model of the human respiratory system. Med Biol Eng Comput 47, 395–404 (2009). https://doi.org/10.1007/s11517-009-0443-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-009-0443-2

Keywords

Search

Navigation