Abstract
The recent development of micro-computers in association with the improvement of data acquisition techniques and signal treatment has made easier the analysis of cerebral electrical activity.
But the methods based on classical harmonic analysis reveal ineffective to detect some activities as epileptiform spike-and-waves of paroxystic origin.
In order to detect those spike-and-waves, we developed a signal treatment based on Model's wavelets. This treatment generates a 2-D representation including the time/frequency componants of the EEG signal splitted into S seconds spans. In these figures, the spike-and-waves arc detected by a neuronal network. The result is then stored into a file, for a delayed use.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
R. Caterini, J.L. Vernet, G. Delhomme, A. Dittmar: A sofware for pattern recognition of skin potentiel responses. Innov. Techn. Med., 13, 3, 256–268, (1992).
P. Clochon, G. Perchey, C. Couque, H. Rebeyrolle, D. Bloyet, P. Etevenon: Automatized classification by neural networks of EGG signals with artifact rejection. 14th annual inlemalionul conference of the IEEE Engineering in Medecine and Biology Society, Satellite symposium on neuroscience and technology, 51–55 (1992).
J.A. Crowe, N.M. Gibson, M.S. Woolfson, M.G. Somek: Wavelet transform as a potential tool for ECG analysis and compression. J Biomed. Eng., 14, 268–272(1992).
P. Gourmelon, D. Clarençon, H. Vignal, J.M. Brun, E. Macioszczyk et L. Fontenil: Intérêt de la transformée en ondelettes dans l'analyse des activités paroxystiques épileptifonnes de l'activité électrique cérébrale. S.S.A. Trav. Scient., 11, 295–296 (1990).
A. Grossmann, J. Morlet, T. Paul: Transforms associated to square inlcgrable group representations. J. Math. Phys. 27, 2473–2479 (1985).
S. Knerr, L. Personnaz, G. Dreyfus, Senior Member, IEEE: Handwritten digit recognition by neural networks with single-layer training. IEEE Transactions on Neural Networks), 1992, in press.
J. Morlet, G. Arens, I. Fourgeau, D. Giard: Wave propagation and sampling theory, Geophysics, 47, 203–206 (1982).
A.W. Przybyszewski: An analysis of the oscillatory patterns in the central nervous system with the wavelet method. J. Neurosci. Methods, 38, 247–257 (1991).
C. Tismer, M. Jobert: The application of wavelet transformation for sleep EEG analysis. Sleep Research, 20A, 518 (1991).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Clochon, P., Clarencon, D., Caterini, R., Roman, V. (1993). Software pattern EEG recognition after a wavelet transform by a neural network. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_230
Download citation
DOI: https://doi.org/10.1007/3-540-56798-4_230
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-56798-1
Online ISBN: 978-3-540-47741-9
eBook Packages: Springer Book Archive