Identification of Slow Wave Propagation in the Multichannel (EGG) Electrogastrographical Signal

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 242)


The aim of this research is to examine the effectiveness of combining two methods Independent Component Analysis (ICA) and adaptive filtering for identifying the slow waves propagation from cutaneous multichannel electrogastrographical signal (EGG). The 3 cycle per minute (3 cpm) gastric pacesetter potential so-called slow wave is fundamental electrical phenomenon of stomach. Slow waves determine the propagation and maximum frequency of stomach contractions. Appropriate spread of gastric contractions is a key for the correct stomach emptying whereas delay this action causes various gastric disorders, such as bloating, vomiting or unexplained nausea. Parameters depict EGG properties mostly based on spectral analysis and information about slow waves spread and coupling are totaly lost, so new methods for studying slow wave propagation are really desired.


electrogastrography slow wave propagation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alvarez, W.C.: The electrogastrogram and what it shows. Journal of the American Medical Association 78(15), 1116–1119 (1922)CrossRefGoogle Scholar
  2. 2.
    Chen, J., McCallum, R.: Electrogastrograpthy: measurment, analysis and prospective applications. Medical & Biomedical Engineering & Computing 29(4), 339–350 (1991)CrossRefGoogle Scholar
  3. 3.
    Chen, J.D.Z., Zou, X., Lin, X., Ouyang, S., Liang, J.: Detection of gastric slow wave propagation from the cutaneous electrogastrogram. American Journal of Physiology: Gastrointestinal and Liver Physiology 277(2), G424–G430 (1999)Google Scholar
  4. 4.
    Comon, P.: Independent component analysis, a new concept? Signal Processing 36(3), 287–314 (1994)CrossRefMATHGoogle Scholar
  5. 5.
    Hyvärinen, A.: New approximations of differential entropy for independent component analysis and projection pursuite. In: Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS 1997), vol. 10, pp. 273–379. MIT Press (1998)Google Scholar
  6. 6.
    Hyvärinen, A.: Survey on independent component analysis. Neural Computing Surveys 2, 94–128 (1999)Google Scholar
  7. 7.
    Hyvärinen, A., Oja, E.: Independent component analysis: Algorithms and applications. Neural Networks 13(4-5), 411–430 (2000)CrossRefGoogle Scholar
  8. 8.
    Koch, K.L., Stern, R.M.: Handbook of Electrogastrography. Oxford University Press (2004)Google Scholar
  9. 9.
    Konturek, S.: Układ trawienny i gruczoły dokrewne. In: Fizjologia człowieka. Wydawnictwo Naukowe DWN, Kraków, Poland (1994)Google Scholar
  10. 10.
    Levanon, D., Chen, J.Z.: Electrogastrography: its role in managing gastric disorder. Journal of Pediatric Gastroenterology & Nutrition 27(4), 431–443 (1998)CrossRefGoogle Scholar
  11. 11.
    Liang, H.: Extraction of gastric slow waves from electrogastrograms: combining independent component analysis and adaptive signal enhancment. Medical & Biomedical Engineering & Computing 43(2), 245–251 (2005)CrossRefGoogle Scholar
  12. 12.
    Liang, J., Chen, J.D.Z.: What can be measured from surface electrogastrography. computer simulations. Digestive Diseases and Sciences 42(7), 1331–1343 (1997)CrossRefGoogle Scholar
  13. 13.
    Parkman, H.P., Hasler, W.L., Barnett, J.L., Eaker, E.Y.: Electrogastrography: a document prepared by the gastric section of the american motility society clinical gi motility testing task force. Neurogastroenterol Motility 15(2), 89–102 (2003)CrossRefGoogle Scholar
  14. 14.
    Wang, Z.S., Cheung, J.Y., Chen, J.D.Z.: Blind separation of multichannel electrogastrograms using independent component analysis based on a neural network. Medical & Biomedical Engineering & Computing 37(1), 80–86 (1999)CrossRefGoogle Scholar
  15. 15.
    Ward, S.M., Sanders, K.M.: Physiology and pathophysiology of interstitial cell of cajal: From bench to bedside. functional development and plasticity of interstitial cells of cajal networks. American Journal of Physiology: Gastrointestinal and Liver Physiology 281(3), G602–G611 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Biomedical Engineering, Department of Biosensors and Processing of Biomedical SignalsSilesian University of TechnologyGliwicePoland

Personalised recommendations