Table of Contents
ISRN Neurology
Volume 2013 (2013), Article ID 287327, 5 pages
http://dx.doi.org/10.1155/2013/287327
Research Article

Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction

1Dosimetry and Medical Equipment Laboratory UB, National Council for Scientific and Technological Research (CONICET), C1426DQG Buenos Aires, Argentina
2Neurosciences Unit, Center for Medical Education and Clinical Research (CEMIC), C1431FWO Buenos Aires, Argentina
3Department of Engineering and Technological Research, National University of La Matanza (UNLaM), B1754JEC San Justo, Argentina

Received 28 March 2013; Accepted 8 May 2013

Academic Editors: R. L. Macdonald, Y. Ohyagi, and E. M. Wassermann

Copyright © 2013 Susana Blanco et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Introduction. Under the hypothesis that the uncontrolled neuronal synchronization propagates recruiting more and more neurons, the aim is to detect its onset as early as possible by signal analysis. This synchronization is not noticeable just by looking at the EEG, so mathematical tools are needed for its identification. Objective. The aim of this study is to compare the results of spectral entropies calculated in different frequency bands of the EEG signals to decide which band may be a better tool to predict an epileptic seizure. Materials and Methods. Invasive ictal records were used. We measured the Fourier spectrum entropy of the electroencephalographic signals 4 to 32 minutes before the attack in low, medium and high frequencies. Results. The high-frequency band shows a markedly rate of increase of the entropy, with positive slopes and low correlation coefficient. The entropy rate of growth in the low-frequency band is practically zero, with a correlation around 0.2 and mostly positive slopes. The mid-frequency band showed both positive and negative slopes with low correlation. Conclusions. The entropy in the high frequencies could be predictor, because it shows changes in the previous moments of the attack. Its main problem is the variability, which makes it difficult to set the threshold that ensures an adequate prediction.