Clustering Based Analysis of Spirometric Data Using Principal Component Analysis and Self Organizing Map

  • Mythili Asaithambi
  • Sujatha C. Manoharan
  • Srinivasan Subramanian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


Spirometry is a valuable tool used for respiratory diagnoses and assessment of disease progression. It measures air flow to help make a definitive diagnosis of pulmonary disorder and confirms presence of airway obstruction. In this work, clustering based classification of spirometric pulmonary function data has been attempted using Principal Component Analysis (PCA) and Self Organising Map (SOM). Pulmonary function data (N=100) are obtained from normal and obstructive subjects using gold standard Spirolab II spirometer. These data are subjected to PCA to extract significant parameters relevant to the cluster structure. The clustering analysis of the significant spirometric parameters is further enhanced using self organizing map and classification of spirometric data is achieved. It is observed from results that FEV1, PEF and FEF25 − 75% are found to be significant in differentiating normal and obstructive subjects. SOM based classification is able to achieve accuracy of 95%. This cluster based method of feature reduction and classification could be useful in assessing the pulmonary function disorders for spirometric pulmonary function test with large dataset.


Spirometry principal component analysis self organizing map clustering 


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  1. 1.
    Gordon, D.: Spirometry: Thinking Beyond the COPD Gold Standard: The Journal of Respiratory Diseases (2012)Google Scholar
  2. 2.
    Sujatha, C.M., Ramakrishnan, S.: Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering. Journal of Medical Engineering & Technology 33(7), 538–543 (2009)CrossRefGoogle Scholar
  3. 3.
    Thomas, L.P.: Benefits of and barriers to the widespread use of spirometry. Current Opinion in Pulmonary Medicine 11, 115–120 (2005)Google Scholar
  4. 4.
    Sujatha, C.M., Mahesh, V., Swaminathan, R.: Comparison of two ANN methods for classification of spirometer data. Measurement Science Review 8(3), 53–57 (2008)Google Scholar
  5. 5.
    Kwan, C., Xu, R., Hayness, L.: A new data clustering and its applications. In: Proceeding of SPIE The International Society for Optical Engineering, vol. 4384, pp. 1–5 (2001)Google Scholar
  6. 6.
    Warren, H., Douglas, T.S.: Fuzzy clustering to detect tuberculous meningitis-associated hyperdensity in CT images. Computational Biology in Medicine 38(2), 165–170 (2007)Google Scholar
  7. 7.
    Ben Hur, A., Guyon, I.: Detecting stable clusters using principal component analysis. In: Brownstein, M.J., Kohodursky, A. (eds.) Functional Genomics: Methods and Protocols, pp. 159–182. Humana Press (2003)Google Scholar
  8. 8.
    Roberts, N.J., Smith, S.F., Partridge, M.R.: Why is spirometry underused in the diagnosis of the breathless patient: a qualitative study. BMC Pulmonary Medicine 11, 37 (2011)CrossRefGoogle Scholar
  9. 9.
    Banthia, A.S., Jayasumana, A.P., Malaiya, Y.K.: Data Size Reduction for Clustering-Based Binning of ICs Using Principal Component Analysis (PCA)Google Scholar
  10. 10.
    Mudassar., A.A., Butt, S.: Application of Principal Component Analysis in Automatic Localization of Optic Disc and Fovea in Retinal Images. Journal of Medical Engineering 2013 (2013), doi:
  11. 11.
    Neware, S., Mehta, K., Zadgaonkar, A.S.: Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier. International Journal of Computer Applications (0975 – 8887) 70(9) (May 2013)Google Scholar
  12. 12.
    Gaibulloev, K., Sandler, T., Sul, D.: Common drivers of transnational terrorism: principal component analysis. Create Research Archive - Published Articles & Papers: Paper 144 (2013)Google Scholar
  13. 13.
    Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analysers. Neural Computation 11, 443–482 (1999)CrossRefGoogle Scholar
  14. 14.
    Koua, E.L.: Using self-organizing maps for information visualization and knowledge discovery in complex geospatial datasets. In: Proceedings of the 21st International Cartographic Conference (ICC ), Durban, South Africa, pp. 1694–1702 (2003)Google Scholar
  15. 15.
    Kiang, M.Y., Kumar, A.A.: Comparative analysis of an extended SOM network and K-means analysis. Journal International Journal of Knowledge-Based and Intelligent Engineering Systems, 9–15 (2004)Google Scholar
  16. 16.
    Kavitha, A., Sujatha, M., Ramakrishnan, S.: Evaluation of flow–volume spirometric test using neural network based prediction and principal component analysis. Journal of Medical System 35, 127–133 (2011)CrossRefGoogle Scholar
  17. 17.
    Kavitha, A., Sujatha, M., Ramakrishnan, S.: Evaluation of Forced expiratory volume prediction in spirometric Test Using Principal Component Analysis. Int. J. Biomedical Engineering and Technology 5(2/3) (2011)Google Scholar
  18. 18.
    Zhang, J., Fang, H.: Using Self-Organizing Maps to Visualize, Filter and Cluster Multidimensional Bio-Omics Data,
  19. 19.
    Marc, T.: A unified continuous optimization framework for centre – based clustering methods. Journal of Machine Learning Research 8, 65–102 (2007)zbMATHGoogle Scholar
  20. 20.
    David, G., Antonio, S., Daniel, R., Alberto, M.C.: Embedded system for diagnosing dysfunctions in the lower urinary tract. In: Proceedings of the ACM Symposium on Applied Computing, Seoul, Korea, pp. 1695–1699 (2007)Google Scholar
  21. 21.
    Aguado, D., Montoy, T., Borras, L., Seco, A., Ferrer, J.: Using SOM and PCA for analyzing and interpreting data from a P-removal SBR. Eng. Appl. Artif. Intel. 21(6), 919–930 (2008)CrossRefGoogle Scholar
  22. 22.
    Haykin, S.: Neural Networks a Comprehensive Foundation, 2nd edn. Pearson Education, India (2008)Google Scholar
  23. 23.
    Kohonen, T.: Self-organizing maps, 3rd edn. Springer (2000)Google Scholar
  24. 24.
    Chattopadhyay, M., Dan, P.K., Mazumdar, S.: Principal component analysis and Self-organizing map for visual clustering Of machine-part cell formation in Cellular manufacturing system. Systems Research Forum 5(1), 25–51 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Mythili Asaithambi
    • 1
  • Sujatha C. Manoharan
    • 2
  • Srinivasan Subramanian
    • 1
  1. 1.Department of Instrumentation Engg.Anna UniversityChennaiIndia
  2. 2.Department of Electronics and Communication EngineeringAnna UniversityChennaiIndia

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