A comparison between the use of ESNN on Long Stereo-EEG Recordings and their largest Lyapunov exponent profiles for epileptic brain analysis

In the last 25 years many works in literature about the capability to detect or predict the occurrence of epileptic seizures, starting from the electroencephalogram (EEG) signal analysis, have often hypothesized that the epileptogenic activity is the result of an abnormal electrical activity hyper-synchronization of different points in an epileptic brain. We already proposed our method to integrate Neural Networks (NN) and the largest Lyapunov exponent (Lmax) for capturing brain dynamics through long stereo-EEG (sEEG) data recorded. In this paper we want to compare the use of a classical Evolving Spiking NN (ESNN) on long sEEG recordings with the integrated method previously proposed. Results are interesting and encourage us to develop, in the next future, a framework for EEG signal analysis.