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.
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© 1993 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/3-540-56798-4_230
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