Abstract
The stomach has a complex physiology, where physical, biological and psychological parameters take part in, thus it is difficult to understand its behavior and function in normal and functional gastrointestinal disorders (FGD). In the area of competitive learning, a large number of models exist which have similar goals but considerably they are different in the way they work. A common goal of these algorithms is to distribute specified number of vectors in a high dimensional space. In this paper several methods related to competitive learning, have been examined by describing and simulating different data distribution. A qualitative comparison of these methods has been performed in the processing of gastric electrical activity (GEA) signal and classifying GEA types to discriminate two GEA types: normal and FGD. The GEA signals are first decomposed into components in different sub-bands using discrete wavelet transformation.
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References
Chui, C.K.: Wavelets: A Tutorial in Theory and Applications. Academic Press, Boston (1992b)
Kaiser, G.: A Friendly Guide to Wavelets. Birkhauser, Basel (1994); ISBN 0-8176-3711-7
Daubechies, I.: Orthonormal Bases of Compactly Supported Wavelets. Communications on Pure and Applied Math. 41, 909–996 (1988)
Daubechies, I.: Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics (1992); ISBN 0-89871-274-2
Wickerhauser, M.V.: Adapted Wavelet Analysis From Theory to Software. A K Peters Ltd, Stanford (1994); ISBN 1-56881-041-5
Addison, P.S.: The Illustrated Wavelet Transform Handbook, Institute of Physics (2002); ISBN 0-7503-0692-0
Vaidyanathan, P.P.: Multirate Systems and Filter Banks. Prentice Hall, Englewood Cliffs (1993); ISBN 0-13-605718-7
Ruskai, M.B.: Wavelets and their Applications. Jones and Bartlett, Boston (1992)
Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE.Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)
Wang, Z.S., Cheung, J.Y., Gao, S.K., Chen, J.D.Z.: Spatio-temporal Nonlinear Modeling of Gastric Electrical Activity. In: The 3rd International Workshop on Biosignal Interpretation (BSI 1999), Chicago, USA, June 12-14 (1999)
Wang, Z.S.: Blind separation of slow waves and spikes from gastrointestinal electrical recordings. IEEE Trans. Inform. Technol. Biomed. 5, 133–137 (2001)
Wang, Z.S., He, Z.Y., Chen, J.D.: Optimized Over Complete Signal Representation and Its Applications to Adaptive Time-Frequency Analysis of EGG. Ann. Biomed. Eng. 26, 859–869 (1998)
Zandi, Y., Nasrabadi, M.A.M.: Neural Network Application in Strange Attractor Investigation to Detect a FGD. In: 4th International IEEE Conference "Intelligent Systems" (2008)
Zandi, Y., Nasrabadi, M.A.M., Jafari, A.H.: Fuzzy Neural Network for Detecting Nonlinear Determinism in Gastric Electrical Activity: Fractal Dimension Approach. In: 4th Inter-national IEEE Conference "Intelligent Systems" (2008)
Zandi, Y., Nasrabadi, M.A.M., Hashemi, M.R.: An Investigation on Chaotic Approach to Detect Functional Gastrointestinal Disorders. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France, pp. 23–26 (August 2007)
Boinee, P., Angelis, A.D., Milotti, E.: Automatic Classification using Self-Organising Neu-ral Networks in Astrophysical Experiments. Dipartimento di Fisicadell’Universit‘a di Udine e INFN, Sez.di Trieste, Gruppo Collegato di Udine, via delle Scienze 208, 33100 Udine, Italy
Marsland, S., Shapirob, J., Nehmzowc, U.: A self-organizing network that grows when required. Neural Networks 15, 1041–1058 (2002)
Liu, Y.: Adaptive self-organized maps based on bidirectional approximate reasoning and its applications to information filtering. Knowledge-Based Systems 19, 719–729 (2006)
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Zandi Mehran, Y., Nafari, M., Zandi Mehran, N., Nafari, A. (2011). A Comparative Analysis and Simulation of Semicompetitive Neural Networks in Detecting the Functional Gastrointestinal Disorder. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22027-2_9
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DOI: https://doi.org/10.1007/978-3-642-22027-2_9
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