Title:Early Detection of Epileptic Seizures in Sparse Domains
Volume: 2
Issue: 1
Author(s): Davide Conigliaro, Paolo Manganotti and Gloria Menegaz
Affiliation:
Keywords:
Early seizure detection, sample entropy, wavelet analysis.
Abstract: 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 seizure. 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 seizure is maintained since all the channels are
separately processed.
