Pattern detection by CNN in epilepsy: recent results

Abstract

0.5% of the world population is suffering from a focal epilepsy. 1 Several actual investigations showed that methods in nonlinear signal processing are important for the derivation of new feature extraction methods to enable the realisation of a portable epilepsy warning system. In this contribution we will present recent results for the pattern detection algorithm which we have proposed in previous investigations. Furthermore the pattern detection algorithm has been transformed in order to run on the first realization of a possible warning device, which had been presented by Laiho et. al.. 2 A detailed discussion of the results will be given in the paper.

Toward an autonomous platform for spatio‐temporal EEG‐signal analysis based on cellular nonlinear networks

“…A seizure precursor may be detected by changes of the so-called signal features derived from EEG signals. Many publications [3][4][5][6][7][8][9][10] have been addressed to this field of research, but the problem remains unsolved. It has been shown that algorithms based on CNN [11][12][13] provided new results in the field of EEG-signal analysis.…”

Automated detection of a preseizure state: non‐linear EEG analysis in epilepsy by Cellular Nonlinear Networks and Volterra systems

“…The main problem, which has been treated in a lot of interdisciplinary investigations, is the determination signal features reliably allowing seizure precursor detection. Although, seemingly the application of recently introduced non-linear analysis procedures [6][7][8][9] provides information predictive of an impending seizure in several cases, the so-called feature extraction problem is still an exciting challenge.…”