Detection of obstructive respiratory abnormality using flow-volume spirometry and radial basis function neural networks

Journal of Medical Systems

Volumn 31, Issue 6, 2007, Pages 461-465

  Veezhinathan, Mahesh ^a   Ramakrishnan, Swaminathan ^a  

^a ANNA UNIVERSITY   (India)

Author keywords

COPD; Flow volume spirometry; Pulmonary abnormality; Radial basis function neural network

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; CHRONIC OBSTRUCTIVE LUNG DISEASE; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; FORCED EXPIRATORY VOLUME; HUMAN; LUNG FUNCTION; PRESSURE VOLUME CURVE; SPIROMETRY;

HUMANS; INDIA; NEURAL NETWORKS (COMPUTER); PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; SPIROMETRY; VITAL CAPACITY;

EID: 35348813762     PISSN: 01485598     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10916-007-9085-9     Document Type: Article

References (26)

1. Pierce, R., Spirometer: An essential clinical measurement. Aust. Fam. Physician. 34:535-539, 2005.
2. David, A. G., and Fan Chung, K., Models of chronic obstructive pulmonary disease. Respir. Res. 5:1-16, 2004.
3. Nicholas, J. G., Chronic obstructive pulmonary disease outcome measurements what's important? what's useful? Proc. Am. Thorac. Soc. 2:267-271, 2005.
4. Hogg, J. C., Chu, F., Utokaparch, S. et al., The nature of small-airway obstruction in chronic obstructive pulmonary disease. N. Engl. J. Med. 350:2645-2653, 2004.
5. Oud, M., and Maarsingh, E. J. W., Spirometer and forced oscillatory assisted optimal frequency band determination for the computerized analysis of tracheal lung sounds in asthma. Physiol. Meas. 25:595-606, 2004.
6. Cooper, B. G., and Madsen, F., Buy. Inspir. 3:40-43, 2000.
7. Suga, H., and Sagawa, K., Instantaneous pressure-volume relationship and their ratio in the excised, supported canine left article. Circ. Res. 34:117, 1974.
8. Barnea, O., Abboud, S., Guber, A., and Bruderman, I., Model-based prediction of expiratory resistance index in patients with asthma. J. Clin. Monit. Comput. 18:241-245, 2004.
9. Abboud S., Barnea O., Guber A., Narkiss N., and Bruderman I., Maximum expiratory flow-volume curve: Mathematical model and experimental results. Med. Eng. Phys. 17:332-336, 1995.
10. Sriraam, N., and Eswaran, C., Context based error modeling for lossless compression of EEG signals using neural networks. J. Med. Syst. 30:439-448, 2006.
11. Ng, E. Y. K., and Sai-Cheong, F., A Framework for early discovery of breast tumor using thermography with artificial neural network. Breast J. 9:341-343, 2003.
12. Kornel, P., Bela, M., Rainer, S., Zalan, D., Zsolt, T., and Janos, F., Application of neural network in medicine. Diagn. Med. Technol. 4:538-546, 1998.
13. Gaetano, P., Artificial neural network in the assessment of respiratory mechanism. Dissertations from faculty of medicine. University of Upsaliensis: Uppsala, 2004.
14. Nicolaos, B. K., and Mary, M. R., On the construction and training of reformulated radial basis function neural networks. IEEE Trans. Neural Netw. 14:835-846, 2003.
15. Chen, T., and Chen, H., Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks. IEEE Trans. Neural Netw. 6:904-910, 1995.
16. Botis, T., and Halkiotis, S., Neural networks for the prediction of spirometric reference values. Med. Inform. Internet Med. 28:299-309, 2003.
17. Juroszek, B., The influence of gas parameters on the result of spirometric test. Meas. Sci. Rev. 5:25-28, 2005.
18. Feyrouz, A., Reena, M., and Peter, J. M., Interpreting pulmonary function tests: Recognize the pattern, and the diagnosis will follow. Clevel. Clin. J. Med. 70:866-881, 2003.
19. Zheng, R. Y., A novel radial basis function neural network for discriminant analysis. IEEE Trans. Neural Netw. 17:604-612, 2006.
20. Wei, Q., Kenneth, S. M. F., Francis, H. Y. C., Lan, F. K., Paul, W. F. P., and Roger, P. H., Adaptive filtering of evoked potentials with radial-basis function neural network prefilter. IEEE Trans. Biomed. Eng. 49:225-232, 2002.
21. Rakesh, K. S., Yogender, A., and Barda, N. D., Backpropagation artificial neural network detects changes in electro-encephalogram power spectra of syncopic patients. J. Med. Syst. 31:63-68, 2007.
22. Joon, L., Stefanie, B., Mike, J. C., David, J. K., Glenn, B., and Tom, C., A radial basis classifier for the automatic detection of aspiration in children. JNER. 3:1-17, 2006.
23. Tchervensky, I. V., De Sobral Cintra, R. J., Neshev, E., Dimitrov, V. S., Sadowski, D. C., and Mintchev, M. P., Center-specific multichannel electrogastrographic testing utilizing wavelet-based decomposition. Physiol. Meas. 27:569-584, 2006.
24. Mahesh, V., and Ramakrishnan, S., Assessment and classification of normal and restrictive respiratory conditions through pulmonary function test and neural network. J. Med. Eng. Technol. 31:300-304, 2007.
25. Ulmer, W. T., Lung function-Clinical importance, problems, and new results. J. Physiol. Pharmacol. 54:11-13, 2003.
26. Enright, P. L., Studnicka, M., and Zielinski, J., Spirometry to detect and manage chronic obstructive pulmonary disease and asthma in primary care settings. Eur. Resp. Monit. 31:1-14, 2005.