A Simple Feature Reduction Method for the Detection of Long Biological Signals

  • Max Chacón
  • Sergio Jara
  • Carlos Defilippi
  • Ana Maria Madrid
  • Claudia Defilippi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


Recent advances in digital processing of biological signals have made it possible to incorporate more extensive signals, generating a large number of features that must be analyzed to carry out the detection, and thereby acting against the performance of the detection methods. This paper introduces a simple feature reduction method based on correlation that allows the incorporation of very extensive signals to the new biological signal detection algorithms. To test the proposed technique, it was applied to the detection of Functional Dyspepsia (FD) from the EGG signal, which is one of the most extensive signals in clinical medicine. After applying the proposed reduction to the wavelet transform coefficients extracted from the EGG signal, a neuronal network was used as a classifier for the wavelet transform coefficients obtained from the EGG traces. The results of the classifier achieved 78.6% sensitivity, and 92.9% specificity for a universe of 56 patients studied.


Hide Layer Discrete Wavelet Transformation Neuronal Network Wavelet Coefficient Functional Dyspepsia 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Blum, A., Langley, P.: Selection of relevant feature and examples in machine learning. Art. Intell. 97, 245–271 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Duda, R., Hart, P.: Patter Classifications, 2nd edn. Wiley, Chichester (2001)Google Scholar
  3. 3.
    Wai-Man, W., Cheng, C., Chu-Yu, B., Chun-Yu Wong, B., Wai-Mo, H.: Non-Ulcer dyspepsia, Med Progress 2, 1–8 (2003)Google Scholar
  4. 4.
    Drossman, D.: ROME II – The Functional Gastrointestinal Disorder, 2nd edn. Degnon Associates, Mc Lean (2000)Google Scholar
  5. 5.
    Liang, J., Chen, J.: What can be measured from surface electrogastrography (computer simulations). Dig. Dis. Sci. 42, 1331–1343 (1997)CrossRefGoogle Scholar
  6. 6.
    Verhagen, M., Van Schelven, L., Samsom, M., Smout, A.: Pitfalls in the analysis of electrogastrographic recording. Gastroenterology 117, 453–460 (1999)CrossRefGoogle Scholar
  7. 7.
    Akin, A., Sun, H.: Non-invasive gastric motility monitor: fast electrogastrogram (fEGG). Phys. Measu. 23, 505–519 (2002)CrossRefGoogle Scholar
  8. 8.
    Chen, J., McCallum, R.: Electrogastrography: Measurement, analysis and prospective application. Med. Biol. Eng. Comput. 29, 339–350 (1991)CrossRefGoogle Scholar
  9. 9.
    Zhiyue, L., Chen, J.D.Z., Parolisi, S., Shifflett, J., Peura, D., McCallum, R.: Prevalence of Gastric Myoelectrical Abnormalities in Patients with Non-Ulcer Dyspepsia and Helicobacter Pylori Infection. Dig. Dis. Sci. 46, 739–745 (2001)CrossRefGoogle Scholar
  10. 10.
    Chen, J., Lin, Z., McCallum, R.: A Noninvasive feature-based detection of delayed gastric emptying in humans using neural networks. IEEE Trans. Biomed. Eng. 49, 409–412 (2000)CrossRefGoogle Scholar
  11. 11.
    Chen, J.: A Computerized data analysis system for electrogastrogram. Comput. Biol. Med. 22, 45–58 (1992)CrossRefGoogle Scholar
  12. 12.
    Graps, A.: An Introduction to Wavelets. IEEE Comput. Sci. Eng. 2, 2–17 (1995)CrossRefGoogle Scholar
  13. 13.
    Mallat, S.: A Theory for Multirresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Patter. Anal. 11, 674–693 (1989)zbMATHCrossRefGoogle Scholar
  14. 14.
    Torrence, C., Compo, G.: A Practical Guide to Wavelet Analysis. Am. Meteorol. Soc. 79, 61–78 (1998)CrossRefGoogle Scholar
  15. 15.
    Gastrosoft Inc, Polygram – Software reference manual. Lower GI Edition. USA (1990)Google Scholar
  16. 16.
    Prince, J., Euliano, N., Lefebre, W.: Neural and Adaptive System. John Wiley & Sons Inc., New York (2000)Google Scholar
  17. 17.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford Claredon Press, Oxford (1995)Google Scholar
  18. 18.
    Flexer, A., Gruber, G., Dorffner, G.: A reliable probabilistic sleep stager based on a single EEG signal. Artif. Intell. Med. 33, 199–207 (2005)CrossRefGoogle Scholar
  19. 19.
    Panerai, R.: Assessment of cerebral pressure autoregulation in humans - a review of measurement methods. Physiological Measurement 19, 305–338 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Max Chacón
    • 1
  • Sergio Jara
    • 1
  • Carlos Defilippi
    • 2
  • Ana Maria Madrid
    • 2
  • Claudia Defilippi
    • 2
  1. 1.Departamento de Ingeniería InformáticaUniversidad de Santiago de ChileSantiagoChile
  2. 2.Hospital ClínicoUniversidad de ChileSantiagoChile

Personalised recommendations