Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 80510, 13 pages
doi:10.1155/2007/80510
Research Article

Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

A. T. Tzallas,1,2 M. G. Tsipouras,2 and D. I. Fotiadis2,3

1Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR 451 10, Greece
2Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Ioannina GR 451 10, Greece
3Biomedical Research Institute, Foundation for Research and Technology-Hellas (BRI-FORTH), University of Ioannina, Ioannina GR 451 10, Greece

Received 31 December 2006; Revised 16 July 2007; Accepted 7 October 2007

Academic Editor: Saied Sanei

Copyright © 2007 A. T. Tzallas 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

The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.