Skip to main content
SpringerLink
Log in

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

  • Conference paper
Man-Machine Interactions 3

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

  • 1755 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alvarez, W.C.: The electrogastrogram and what it shows. Journal of the American Medical Association 78(15), 1116–1119 (1922)

    Article  Google Scholar 

  2. Chen, J., McCallum, R.: Electrogastrograpthy: measurment, analysis and prospective applications. Medical & Biomedical Engineering & Computing 29(4), 339–350 (1991)

    Article  Google Scholar 

  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. Comon, P.: Independent component analysis, a new concept? Signal Processing 36(3), 287–314 (1994)

    Article  MATH  Google Scholar 

  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. Hyvärinen, A.: Survey on independent component analysis. Neural Computing Surveys 2, 94–128 (1999)

    Google Scholar 

  7. Hyvärinen, A., Oja, E.: Independent component analysis: Algorithms and applications. Neural Networks 13(4-5), 411–430 (2000)

    Article  Google Scholar 

  8. Koch, K.L., Stern, R.M.: Handbook of Electrogastrography. Oxford University Press (2004)

    Google Scholar 

  9. Konturek, S.: Układ trawienny i gruczoły dokrewne. In: Fizjologia człowieka. Wydawnictwo Naukowe DWN, Kraków, Poland (1994)

    Google Scholar 

  10. Levanon, D., Chen, J.Z.: Electrogastrography: its role in managing gastric disorder. Journal of Pediatric Gastroenterology & Nutrition 27(4), 431–443 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Faculty of Biomedical Engineering, Department of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Barbara T. Mika & Ewaryst J. Tkacz

Authors
  1. Barbara T. Mika
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ewaryst J. Tkacz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Barbara T. Mika .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dr. Aleksandra Gruca

  2. Polish Academy of Sciences and Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Mika, B.T., Tkacz, E.J. (2014). Identification of Slow Wave Propagation in the Multichannel (EGG) Electrogastrographical Signal. In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02309-0_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02308-3

  • Online ISBN: 978-3-319-02309-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation