This work presents a method for early detection of epileptic seizures from EEG data, taking into account
information about both the temporal and the spatial evolution of the seizures. The system was designed
using over 8 hours of EEG, including 10 seizures in 5 patients. Seizure detection was accomplished in three
main stages: multiresolution overcomplete decomposition by the à-trous algorithm, feature extraction by
computing power spectral density and sample entropy values of subbands and detection by using z-test and
support vector machines (SVM). Results highlight large differences between the sub-band sample entropy
values during ictal and normal EEG epochs, respectively, reveling a substantial increase of such parameter
during the crisis. This enables high detection accuracy and specificity especially in beta and gamma bands
(16-125 Hz). The detection performance of the proposed method was evaluated based on the ground truth
provided by the expert neurophysiologist, and the results show that our technique is capable to obtain
a high accuracy (above the 95% on average), with a high temporal resolution. This enables reaching
very low detection latency and early detection of the seizures onset. Furthermore, spatial information,
within the limits of the acquisition, on the evolution of the crisis is maintained since all the channels are
separately processed.
