Computer Methods and Programs in Biomedicine
Volume 105, Issue 3 , Pages 183-193 , March 2012

Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease

  • Jorge L.M. Amaral

      Affiliations

    • Department of Electronics and Telecommunications Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil
    • ,
  • Agnaldo J. Lopes

      Affiliations

    • Pulmonary Function Laboratory, Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil
    • ,
  • José M. Jansen

      Affiliations

    • Pulmonary Function Laboratory, Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil
    • ,
  • Alvaro C.D. Faria

      Affiliations

    • Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), State University of Rio de Janeiro, Rio de Janeiro, Brazil
    • ,
  • Pedro L. Melo

      Affiliations

    • Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), State University of Rio de Janeiro, Rio de Janeiro, Brazil
    • Corresponding Author InformationCorresponding author.

Received 1 April 2011 ,Revised 15 August 2011 ,Accepted 22 September 2011.

References 

  1. The global initiative for chronic obstructive lung disease. Available from: <http://www.goldcopd.com> (accessed March 2011).
  2. World Health Organization. Available from: <http://www.who.int/respiratory/copd/burden/en/index.html> (accessed March 2011).
  3. Enright PL, Crapo RM. Controversies in the use of spirometry for early recognition and diagnosis of chronic obstructive pulmonary disease in cigarette smokers. Clin. Chest Med. 2000;21(4):645–652
  4. Ljung L. System Identification: Theory for the User. Londres: Prentice-Hall Inc.; 1987;
  5. Dubois AB, Brody AW, Lewis DH, Burges BF. Oscillation mechanics of lungs and chest in man. J. Appl. Physiol. 1956;8:587–594
  6. Faria ACD, Lopes AJ, Jansen JM, Melo PL. Evaluating the forced oscillation technique in the detection of early smoking-induced respiratory changes. Biomed. Eng. Online. 2009;25:8–22
  7. Orr AJ, Westenskow DR. A breathing circuit alarm system based on neural networks. J. Clin. Monit. 1994;10:101–109
  8. Bright P, Miller MR, Franklyn JA, Sheppard MC. The use of a neural network to detect upper airway obstruction caused by goiter. Am. J. Respir. Crit. Care Med. 1998;157:1885–1891
  9. Leon MA, Räsänen J, Mangar D. Neural network-based detection of esophageal intubation. Anesth. Analg. 1994;78:548–553
  10. Räsänen J, León MA. Detection of lung injury with conventional and neural network-based analysis of continuous data. J. Clin. Monit. 1998;14:433–439
  11. Perchiazzi G, Högman M, Rylander C, Giuliani R, Fiore T, Hedenstierna G. Assessment of respiratory system mechanics by artificial neural networks: an exploratory study. J. Appl. Physiol. 2001;90:1817–1824
  12. Uncü U. Evaluation of pulmonary function tests by using fuzzy logic theory. J. Med. Syst. 2010;34(3):241–250
  13. Lopes AJ, Capone D, Mogami R, Lanzillotti RS, Melo PL, Jansen JM. Severity classification for idiopathic pulmonary fibrosis by using fuzzy logic. Clinics. 2011;66(6):1015–1019
  14. Di Mango AMGT, Lopes AJ, Jansen JM, Melo PL. Changes in respiratory mechanics with degrees of airway obstruction in COPD: detection by forced oscillation technique. Respir. Med. 2006;100(3):399–410
  15. Ionescu C, Derom E, De Keyser R. Assessment of respiratory mechanical properties with constant-phase models in healthy and COPD lungs. Comput. Methods Programs Biomed. 2010;97(1):78–85
  16. Barúa M, Nazeran H, Nava P, Granda V, Diong B. Classification of pulmonary diseases based on impulse oscillometric measurements of lung function using neural networks. In: Conf. Proc. IEEE Eng. Med. Biol. Soc.. 2004;p. 3848–3851
  17. Barúa M, Nazeran H, Nava P, Diong B, Goldman M. Classification of impulse oscillometric patterns of lung function in asthmatic children using artificial neural networks. In: Conf. Proc. IEEE Eng. Med. Biol. Soc.. 2005;p. 327–331
  18. Macleod D, Birch M. Respiratory input impedance measurements: forced oscillation methods. Med. Biol. Eng. Comput. 2001;39:505–516
  19. Hellinckx J, Cauberghs M, De Boeck K, Demedts M. Evaluation of impulse oscillation system: comparison with forced oscillation technique and body plethysmography. Eur. Respir. J. 2001;18:564–570
  20. Amaral JLM, Faria ACD, Lopes AJ, Jansen JM, Melo PL. Automatic identification of chronic obstructive pulmonary disease based on forced oscillation measurements and artificial neural networks. In: 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina. 2010;
  21. Kuncheva LI. Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience; 2004;
  22. Duda RO, Hart PE, Stork DG. Pattern Classification. Wiley-Interscience; 2000;
  23. Tan PN, Steinbach M, Kumar V. Introduction to Data Mining. University of Minnesota Publisher, Addison-Wesley Copyright; 2006;
  24. Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed.. Morgan Kaufmann; 2005;
  25. Haykin S. Neural Networks a Comprehensive Foundation. Englewood Cliffs: Macmillan College Publishing Company; 1994;
  26. Vapnik VN. The Nature of Statistical Learning Theory. 2nd ed.. New York: Springer; 2000;
  27. Zhang GP. Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 2000;30:451–462
  28. Pedreira CE, Macrini L, Land MG, Costa ES. New decision support tool for treatment intensity choice in childhood acute lymphoblastic leukemia. IEEE Trans. Inf. Technol. Biomed. 2009;13:284–290
  29. Goldbaum MH, Sample PA, Chan K, Williams J, Lee T-W, Blumenthal E, et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. Invest. Ophthalmol. Vis. Sci. 2002;43(1):162–169
  30. Guyon I, Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003;3:1157–1182
  31. Dietterich DT. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998;10:1895–1923
  32. Fawcett T. An introduction to roc analysis. Pattern Recogn. Lett. 2006;27(8):861–874
  33. Refaeilzadeh P, Tang L, Liu H. Cross Validation, Encyclopedia of Database Systems. Springer; 2009;
  34. Demsar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 2006;7:1–30
  35. Cavalcanti JV, Lopes AJ, Jansen JM, Melo PL. Detection of changes in respiratory mechanics due to increasing degrees of airway obstruction in asthma by the forced oscillation technique. Respir. Med. 2006;100(12):2207–2219
  36. Melo PL. New impedance spectrometer for scientific and clinical studies on the respiratory system. Rev. Sci. Instrum. 2000;71(7):2867–2872
  37. Melo PL, Werneck MM, Giannella-Neto A. Influence of the pressure generator non-linearities in the accuracy of respiratory input impedance measured by forced oscillation. Med. Biol. Eng. Comput. 2000;38:102–108
  38. Faria ACD, Lopes AJ, Jansen JM, Melo PL. Assessment of respiratory mechanics in patients with sarcoidosis using forced oscillation: correlations with spirometric and volumetric measurements and diagnostic accuracy. Respiration. 2009;78(1):93–104
  39. Duin RPW, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax DMJ, et al. PRTools4.1, A Matlab Toolbox for Pattern Recognition. Delft University of Technology; 2007;
  40. C.W. Hsu, C.C. Chang, C.J. Lin, A practical guide to support vector classification. Available from: <www.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf> (accessed October 2010).
  41. Martinez WL, Martinez AR. Exploratory Data Analysis with MATLAB. CRC Press; 2005;
  42. Conover WJ. Practical Nonparametric Statistics. 3rd ed.. Wiley; 1999;
  43. Delany SJ, Cunningham P, Coyle L. An assessment of case-based reasoning for spam filtering. Artif. Intell. Rev. 2005;24(3):359–378
  44. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36
  45. Swets JA, Picket RM. Evaluation of diagnostic systems: methods from signal detection theory. Med. Phys. 1983;10(2):266–267
  46. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240:1285–1293
  47. Golpe R, Jiménez A, Carpizo R, Cifrian JM. Utility of home oximetry as a screening test for patients with moderate and severe symptoms of obstructive sleep apnea. Sleep. 1999;22(7):932–937
  48. Croxton TL, Weinmann GG, Senior RM, Hoidal JR. Future research directions in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2002;165:838–844

PII: S0169-2607(11)00256-2

doi: 10.1016/j.cmpb.2011.09.009

Computer Methods and Programs in Biomedicine
Volume 105, Issue 3 , Pages 183-193 , March 2012

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