Epileptic seizure detection in EEGs using time-frequency analysis.
The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency (t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t-f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented.
Tzallas
Alexandros T
AT
Unit of Medical Technology and Intelligent Information Systems, Department of Material Science and Technology, University of Ioannina, Ioannina 45110, Greece. atzallas@cc.uoi.gr
Tsipouras
Markos G
MG
Fotiadis
Dimitrios I
DI
D001499:Bayes Theorem; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis*; D005583:Fourier Analysis; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer