Skip to main content
SpringerLink
Log in

Evaluation of Flow–Volume Spirometric Test Using Neural Network Based Prediction and Principal Component Analysis

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

In this work, an attempt has been made to enhance the diagnostic relevance of spirometric pulmonary function test using neural networks and Principal Component Analysis (PCA). For this study, flow–volume curves (N = 175) using spirometers were generated under standard recording protocol. A method based on neural network is used to predict the most significant parameter, FEV1. Further, PCA is used to analyze the interdependency of the parameters in the measured and predicted datasets. Results show that the back propagation neural network is able to predict FEV1 both in normal and abnormal cases. The variation in the magnitude and direction of parameters in the contribution of the principal components shows that FEV1 is a significant discriminator of normal and abnormal datasets and is further confirmed by the percentage variance in the first few principal components. It appears that this method of prediction and principal component analysis on the measured and predicted datasets could be useful for spirometric pulmonary function test with incomplete data.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Crapo, R. O., Pulmonary—function testing. N. Eng. J. Med. 331:25–30, 1994.

    Article  Google Scholar 

  2. Miller, M. R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., Crapo, R., Enright, P., van der Grinten, C. P. M., Gustafsson, P., Jensen, R., Johnson, D. C., MacIntyre, N., McKay, R., Navajas, D., Pedersen, O. F., Pellegrino, R., Viegi, G., and Wanger, J., Standardisation of spirometry. Eur. Resp. J. 26:319–338, 2005.

    Article  Google Scholar 

  3. Wagner, N. L., Beckett, W. S., and Steinberg, R., Using spirometry results in occupational medicine and research: common errors and good practice in statistical analysis and reporting. Ind. J. Occ. Environ. Med. 10:5–10, 2006.

    Article  Google Scholar 

  4. Arora, V. K., and Raghu, S., Flow volume curves: clinical significance. Lung Ind. 14:169–171, 2000.

    Google Scholar 

  5. Feyrouz, A., Reena, M., and Peter, J. M., Interpreting pulmonary function tests: recognise the pattern, and the diagnosis will follow. Cleve. Clin. J. Med. 70:866–880, 2003.

    Article  Google Scholar 

  6. Pierce, R., Spirometer: an essential clinical measurement. Aust. Fam. Phys. 34:535–539, 2004.

    Google Scholar 

  7. David, P. J., and Rob, P., Spirometry—The measurement and interpretation of ventilatory function in clinical practice. Spirometry Handbook, 3rd edition. 1–24, 2008.

  8. Timothy, J. B., and Irene, P., An approach to interpreting spirometry. Am. Fam. Phys. 69:1108–1114, 2004.

    Google Scholar 

  9. Dimitrios, P., Georgios, E., Kiriakos, K., Nikolas, Z., Panos, G., and Kostas, S., Lung function measurements in traditional bakers. Acta Biomed. 79:197–203, 2008.

    Google Scholar 

  10. Ulmer, W. T., Lung function—clinical importance, problems and new results. J. Physiol. Pharmacol. 54:11–13, 2003.

    Google Scholar 

  11. Jesu, C. J., and Ramakrishnan, S., Assessment and classification of mechanical strength components of human trabecular bone using digital image processing and neural networks. J. Mech. Med. Biol. 7:315–324, 2007.

    Article  Google Scholar 

  12. Sujatha, C. M., and Ramakrishnan, S., Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering. J. Med. Syst. (Under Print— 10.1007/s10916-008-9196-y), 2008.

  13. Sachin, M. B., SangChul, P., and Gi-Nam, W., Predicting extrusion process parameters using neural networks. Int. J. Mech. Syst. Sci. Eng. 1:161–167, 2007.

    Google Scholar 

  14. Benardos, P. G., and Vosniakos, G. C., Optimizing feed forward artificial neural network architecture. Eng. App Art. Int. 20:365–382, 2007.

    Article  Google Scholar 

  15. Rakesh, K. S., Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. J. Med. Syst. 32:291–299, 2008.

    Article  Google Scholar 

  16. Gaetano, P., Marieann, H., Christian, R., Rocco, G., Tommaso, F., and Goron, H., Assessment of respiratory system mechanics by artificial neural networks: an exploratory study. J. App. Physiol. 90:1817–1824, 2001.

    Article  Google Scholar 

  17. Lisboa, P. J. G., Emmanuel, C., Ifeachor, and Piotr, S. S., Artificial neural networks in biomedicine. Art. Int. Med. 25:211–214, 2002.

    Article  Google Scholar 

  18. Mahesh, V., Sujatha, C. M., and Ramakrishnan, S., Experimental analysis on human respiratory dynamics using flow volume spirometry and combined neural networks. J. Mech. Med. Biol. 8:541–548, 2008.

    Article  Google Scholar 

  19. Ferrigno, G., and Carnevali, P., Principal component analysis of chest wall movement in selected pathologies. Med. Biol. Eng. Comp. 36:445–451, 1998.

    Article  Google Scholar 

  20. Salaffi, F., Manganelli, P., Carotti, M., and Baldelli, S., The differing patterns of subclinical pulmonary involvement in connective tissue diseases as shown by application of factor analysis. Clin. Rheumat. 19:35–41, 2000.

    Google Scholar 

  21. Marek, S., Pniewski, Emilia, K., Pawel, Z., Katarzyna, S., Agata, P., Mariusz, K., and Bogdan, B., Pattern recognition methods in evaluation of the structure of the laboratory data biominerals, antioxidant enzymes, selected biochemical parameters, and pulmonary function of welders. Biol. Trace Elem. Res. 93:39–46, 2003.

    Article  Google Scholar 

  22. Arnaz, M., and Robert, X. G., PCA-based feature selection scheme for machine defect classification. IEEE Trans. Inst. Meas. 53:1517–1525, 2004.

    Article  Google Scholar 

  23. Terry, E. R., Ann, N. L., William, H. N., Francis, G. B., Frandics, P. C., Daniel, A. B., Tyson, H. H., and Richard, B. M., Composite spirometric–computed tomography outcome measure in early cystic fibrosis lung disease. Am. J. Resp. Crit. Car. Med. 188:688–693, 2003.

    Google Scholar 

  24. Jenkins, C. R., Thien, F. C. K., Wheatley, J. R., and Reddel, H. K., Traditional and patient—centred outcomes with three classes of asthma medication. Eur. Resp. J. 26:36–44, 2005.

    Article  Google Scholar 

  25. Cooper, B. G., and Madsen, F. Eur. Resp. buyers guide. 3:40–43, 2000.

  26. Igor, B., Principal component analysis is a powerful instrument in occupational hygiene enquiries. Ann. Occup. Hyg. 48:655–661, 2004.

    Article  Google Scholar 

  27. Samanwoy, G. D., Hojjat, A., and Nahid, D., Principal component analysis—enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Biomed. Eng. 50:512–518, 2008.

    Google Scholar 

  28. Aguado, D., Montoy, T., Borras, L., Seco, A., and Ferrer, J., Using SOM and PCA for analyzing and interpreting data from a P-removal SBR. Eng. App. Art. Int. 21:919–930, 2008.

    Article  Google Scholar 

  29. Gabriel, The biplot graphic display of matrices with application to principal component analysis. Biometrika. 58:453–467, 1971.

    Article  MATH  MathSciNet  Google Scholar 

  30. Daniel, C. G., and Jonathan, D. T., Clinical review: respiratory mechanics in spontaneous and assisted ventilation. Crit. Car. 9:472–484, 2005.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Instrumentation Engineering, Madras Institute of Technology Campus, Anna University, Chennai, 600044, India

    Anandan Kavitha & Swaminathan Ramakrishnan

  2. Department of Electronics and Communication Engineering, CEG Campus, Anna University, Chennai, India

    Manoharan Sujatha

Authors
  1. Anandan Kavitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manoharan Sujatha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Swaminathan Ramakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swaminathan Ramakrishnan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kavitha, A., Sujatha, M. & Ramakrishnan, S. Evaluation of Flow–Volume Spirometric Test Using Neural Network Based Prediction and Principal Component Analysis. J Med Syst 35, 127–133 (2011). https://doi.org/10.1007/s10916-009-9349-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9349-7

Keywords

Search

Navigation