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Journal of Medical Systems
Published in: Journal of Medical Systems 4/2007

01-08-2007

Analysis of the Electrogastrogram Using Discrete Wavelet Transform and Statistical Methods to Detect Gastric Dysrhythmia

Author: Mahmut Tokmakçi

Published in: Journal of Medical Systems | Issue 4/2007

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Abstract

Electrogastrography (EGG) is a method of recording stomach electrical activity from cutaneous electrodes placed on the abdominal surface. Compared with other electrophysiological measurements, such as electrocardiography, the progress of the applicability of the EGG has been very slow. Unlike imaging or manometrical studies, stomach motility disorders are not diagnosed based only on abnormal EGG parameters. Limitations of EGG recording, processing, computation, acceptable normal parameters, technique and reading should be known to conduct subjective assessments when EGG is used to resolve stomach dysfunction. Therefore appropriate application of non-invasive EGG should go on providing more information and insight in understanding these limitations. And so the aim of this study were to contribute the evolution of the EGG to enter the clinical world as a routine check-up method and to develop new time-frequency analysis method for the detection of gastric dysrhythmia from the EGG.
Literature
1.
Jebbink, R. J., Samsom, M., Bruijs, P. P., et al., Hyperglycemia induces abnormalities of gastric myoelectrical activity in patients with type I diabetes mellitus. Gastroenterology 107:1390–1397, 1994.
2.
Szurszewski, J. H., Electrical basis for gastrointestinal motility. In: Johson, L. R., (Ed.), Physiology of the Gastrointestinal Tract. Vol. I, 2nd ed., pp. 383–422. New York: Raven Press, 1987.
3.
Cheung, B., and Vaitkus, P., Perspectives of electrogastrography and motion sickness. Brain Res. Bull. 47:421–431, 1998.CrossRef
4.
Koch, K. L., Diabetic gastropathy-gastric neuromuscular dysfunction in diabetes mellitus a review of symptoms, pathophysiology, and treatment. Dig. Dis. Sci. 44(6):1061–1075, 1999.CrossRef
5.
van der Voort, I. R., Osmanoglou, E., Seybold, M., Heymann-Monnikes, I., et al., Electrogastrography as a diagnostic tool for delayed gastric emptying in functional dyspepsia and irritable bowel syndrome. Neurogastroenterol. Motil. 15:467–473, 2003.CrossRef
6.
Orr, W. C., Zhang, M., McClanahan, J., Sloan, S., and Chen, J. D., Gastric myoelectric activity in older adults treated with cisapride for gastro-oesophageal reflux disease. Aliment. Pharmacol. Ther. 14:337–343, 2000.CrossRef
7.
Hamilton, J. W., Bellahsene, B., and Reichelderfer, M., Human electrogastrograms: Comparison of surface and mucosal recordings. Dig. Dis. Sci. 9:31–33, 1986.
8.
Smout, A. J., van der Schee, E. J., and Grashuis, J. L., What is measured in electrogastrography. Dig. Dis. Sci. 25:179–187, 1980.CrossRef
9.
Familoni, B. A., Bowes, K. L., Kingma, Y. J., and Cote, K. R., Can transcutaneous recordings detect gastric electrical abnormalities? Gut 32:141–146, 1991.
10.
Alvarez, W. C., The electrogastrogram and what it shows. JAMA 78:1116–1119, 1922.
11.
Chang, F. Y., Electrogastrography: Basic knowledge, recording, processing, and its clinical applications. J. Gastroenterol. Hepatol. 20:502–516, 2005.CrossRef
12.
Kara, S., Dirgenali, F., and Okkesim, Ş., Estimating gastric rhythm differences using a wavelet method from the electrogastrograms of normal and diabetic subjects. Instrum. Sci. Technolog. 33:519–532, 2005.CrossRef
13.
van der Schee, E. T., and Grashius, J. L., Running spectral analysis as an aid in the representation and interpretation of electrogastrographic signals. Med. Biol. Eng. Comput. 25:57–62, 1987.
14.
Pfister, C. J., Hamilton, J. W., Nagel, N., Bass, P., Webster, J. G., and Tompkins, W. J., Use of spectral analysis in the detection of frequency differences in the electrogastrograms of normal and diabetic subjects. IEEE Trans. Biomed. Eng. 35:935–941, 1988.
15.
Price, C. N., Westwick, D. T., and Mintchev, M. P., Analysis of canine model of gastric electrical uncoupling using recurrence quantification analysis. Dig. Dis. Sci. 50:885–892, 2005.CrossRef
16.
Leahy, A., Besherdas, K., Clayman, C., Mason, I., and Epstein, O., Abnormalities of the electrogastrogram in functional gastrointestinal disorders. Am. J. Gastroenterol. 94:1023–1028, 1999.
17.
Nawap, S. H., and Quatieri, T. F., In: Lim, J. S., and Oppenheim, A. V., (Eds.), Short Time Fourier Transform, pp. 239–337. Englewood Cliffs, NJ: Prentice-Hall, 1988.
18.
Kara, S., Dirgenali, F., and Okkesim, S., Estimation of wavelet and short-time Fourier transform sonograms of normal and diabetic subjects’ electrogastrogram. Comput. Biol. Med. 36:1289–1302, 2006.
19.
Daubechies, I., The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5):961–1005, 1990.MATHCrossRefMathSciNet
20.
Soykan, I., Lin, Z., Sarosiek, I., and McCallum, R. W., Gastric myoelectrical activity, gastric emptying, and correlations with symptoms and fasting blood glucose levels in diabetic patient. Am. J. Med. Sci. 317(4):226–231, 1999.CrossRef
21.
Girault, J. M., Kouame, D., Ouahabi, A., and Patat, F., Micro-emboli detection: An ultrasound Doppler signal processing viewpoint. IEEE Trans. Biomed. Eng. 47:1431–1439, 2000.CrossRef
22.
Zhang, Y., Wang, Y., Wang, W., and Liu, B., Doppler ultrasound signal denoising based on wavelet frames. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 48:709–716, 2001.CrossRef
23.
Welch, P. D., The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short modified periodograms. IEEE Trans. Audio Electroacoust. 15:70–73, 1967.CrossRefMathSciNet
24.
Hu, G., Digital Signal Processing—Theory, Algorithm and Actualization. Qinghua University Press: Beijing, China, 1998.
25.
Cheng, J., and Lan, C., Experimental studies on sound and vibration of a two-tone Chinese Peace Bell. J. Sound Vib. 261:351–358, 2003.CrossRef
26.
Stern, R. M., Koch, K. L., Stewart, W. R., and Lindblad, I. M., Spectral analysis of tachygastria recorded during motion sickness. Gastroenterology 92:92–97, 1987.
Metadata
Title
Analysis of the Electrogastrogram Using Discrete Wavelet Transform and Statistical Methods to Detect Gastric Dysrhythmia
Author
Mahmut Tokmakçi
Publication date
01-08-2007
Published in
Journal of Medical Systems / Issue 4/2007
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-007-9069-9

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