Skip to main content
SpringerLink
Log in

Filter Banks and Neural Network-based Feature Extraction and Automatic Classification of Electrogastrogram

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

Dysrhythmia in gastric myoelectrical activity has been frequently observed in patients with gastric motor disorders and gastrointestinal symptoms. The assessment of the regularity of gastric myoelectrical activity is of great clinical significance. The aim of this study was to develop an automated assessment method for the regularity of gastric myoelectrical activity from the surface electrogastrogram (EGG). The method proposed in this paper was based on the filter bank and neural network. First, the EGG signal was divided into frequency subbands using filter bank analysis. Second, a parameter called the subband energy ratio (SER) was computed for each subband signal. A multilayer perceptron neural network was then used to automatically classify the EGG signal into four categories: bradygastria, normal, tachygastria, and arrhythmia, using the SER as the input. The EGG recording was made using the standard method of electrogastrography by placing electrodes on the abdominal surface. The study was performed in 40 patients with various gastric motor disorders, ten healthy adults, and ten healthy children. The neural network was trained and tested using the EGG data obtained from the patients. The regularity of gastric myoelectrical activity was assessed based on the classification of the minute-by-minute EGG segments. Using the running spectral analysis method as a gold standard, the proposed automated method had an accuracy of 100% for the training set and 97% for the test set. It was concluded that the proposed method provides an accurate and automatic assessment of the regularity of gastric myoelectrical activity from the EGG. © 1999 Biomedical Engineering Society.

PAC99: 8780-y, 8717-d, 0705Mh, 0270Hm

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

REFERENCES

  1. Abell, T. L., and J. R. Malagelada. Electrogastrography: Current assessment and future perspectives. Dig. Dis. Sci. 33:982–992, 1988.

    Google Scholar 

  2. Chen, J. D. Z., and R. W. McCallum. Clinical applications of electrogastrography. Am. J. Gastroenterol. 88:1324–1336, 1993.

    Google Scholar 

  3. Chen, J. D. Z., and R. W. McCallum (Eds.). In: Electrogastrography: Principles and Applications. New York: Raven, 1994.

    Google Scholar 

  4. Chen, J. D. Z., R. W. McCallum, and R. Richards. Frequency components of the electrogastrogram and their correlation with gastrointestinal contractions in humans. Med. Biol. Eng. Comput. 31:60–67, 1993.

    Google Scholar 

  5. Chen, J. D. Z., et al. Adaptive spectral analysis of cutaneous electrogastric signals using autoregressive moving average modeling. Med. Biol. Eng. Comput. 28:531–536, 1990.

    Google Scholar 

  6. Chen, J. D. Z., Z. W. R. Stewart, and R. W. McCallum. Adaptive spectral analysis of episodic rhythmic variations in the cutaneous electrogastrogram. IEEE Trans. Biomed. Eng. 40:128–135, 1993.

    Google Scholar 

  7. Chen, J. D. Z., Z. Y. Lin, Q. Wu, and R. W. McCallum. Non-invasive identification of gastric contractions from surface electrogastrogram using back propagation neural networks. Med. Eng. Phys. 17:219–225, 1995.

    Google Scholar 

  8. Chen, J., B. D. Schirmer, and R. W. McCallum. Serosal and cutaneous recordings of gastric myoelectrical activity in patients with gastroparesis. Am. J. Physiol. 266:G90–98, 1994.

    Google Scholar 

  9. Familoni, B. O., K. L. Bowes, Y. J. Kingma, and K. R. Cote. Can transcutaneous recordings detect gastric electrical abnormalities? Gut 32:141–6, 1991.

    Google Scholar 

  10. Hsin, H. C., C. C. Li, M. Sun, and R. Sclabassi. An adaptive training algorithm for back-propagation neural networks. IEEE Trans. Syst. Man Cybern. 25:512–514, 1995.

    Google Scholar 

  11. Hush, D. R., and B. G. Horne. Progress in supervised neural networks. IEEE Signal Process. Mag. 10:8–39, 1993.

    Google Scholar 

  12. Iiguni, Y., H. Sakai, and H. Tokumaru. A real-time learning algorithm for a multilayered neural network based on the extended Kalman filter. IEEE Trans. Signal Process. 40:959–966, 1992.

    Google Scholar 

  13. Koilpillai, R. D., and P. P. Vaidyanathan. Cosine-modulated FIR filter banks satisfying perfect reconstruction. IEEE Trans. Signal Process. 40:770–783, 1992.

    Google Scholar 

  14. Liang, J., J. Y. Cheung, and J. D. Z. Chen. Detection and elimination of motion artifacts in electrogastrogram using feature analysis and neural networks. Ann. Biomed. Eng. 25:850–857, 1997.

    Google Scholar 

  15. Liang, J., and J. D. Z. Chen. What can be measured from surface electrogastrography: Computer simulations. Dig. Dis. Sci. 42:1331–1343, 1997.

    Google Scholar 

  16. Lin, Z. Y., and J. D. Z. Chen. Time-frequency representation of the electrogastrogram-Application of the exponential distribution. IEEE Trans. Biomed. Eng. 41:267–275, 1994.

    Google Scholar 

  17. Mao, J., and A. K. Jain. Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans. Neural Netw. 6:296–317, 1995.

    Google Scholar 

  18. Mintchev, M. P., Y. J. Kingma, and K. L. Bowes. Accuracy of cutaneous recordings of gastric electrical activity. Gastroenterology 104:1273–1280, 1993.

    Google Scholar 

  19. Mintchev, M. P., S. J. Otto, and K. L. Bowes. Electrogastrography can recognize gastric electrical uncoupling in dog. Gastroenterology 112:2006–11, 1997.

    Google Scholar 

  20. Nguyen, T. Q., and R. D. Koipillai. The theory and design of arbitrary-length cosine-modulated filters and wavelets satisfy perfect reconstruction. IEEE Trans. Signal Process. 44:473–483, 1996.

    Google Scholar 

  21. Scalero, R. S., and N. Tepedelenlioglu. A fast new algorithm for training feed forward neural networks. IEEE Trans. Signal Process. 40:202–210, 1992.

    Google Scholar 

  22. Smout, A. J. P. M., E. J. van der Schee, and J. L. Grashuis. What is measured in electrogastrography? Dig. Dis. Sci. 25:179–187, 1980.

    Google Scholar 

  23. Vaidyanathan, P. P. Multirate Systems and Filter Banks. Englewood Cliffs, NJ: Prentice-Hall, 1993.

    Google Scholar 

  24. van der Schee, E. J., and J. L. Grashuis. Running spectrum analysis as an aid in the representation and interpretation of electrogastrographic signals. Med. Biol. Eng. Comput. 25:57–62, 1987.

    Google Scholar 

  25. Vetterli, M. A theory of multirate filter banks. IEEE Trans. Acoust., Speech, Signal Process. 35:356–372, 1987.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lynn Institute for Healthcare Research, Oklahoma City, OK

    Zhishun Wang & J. D. Z. Chen

  2. Department of Radio Engineering, Southeast University, Nanjing, People's Republic of China

    Zhenya He

Authors
  1. Zhishun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenya He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. D. Z. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Z., He, Z. & Chen, J.D.Z. Filter Banks and Neural Network-based Feature Extraction and Automatic Classification of Electrogastrogram. Annals of Biomedical Engineering 27, 88–95 (1999). https://doi.org/10.1114/1.151

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1114/1.151

Search

Navigation