Classification of electroencephalogram signals with combined time and frequency features

Epilepsy is a neurological disorder that causes people to have seizures and the main application field of electroencephalography. In this study, combined time and frequency features approach for the classification of healthy and epileptic electroencephalogram (EEG) signals is proposed. Features in the time domain are extracted using the cross correlation (CC) method. Features related to the frequency domain are extracted by calculating the power spectral density (PSD). In the study, these individual time and frequency features are considered to carry complementary information about the nature of the EEG itself. By using divergence analysis, distributions of the feature vectors in the feature space are quantitatively measured. As a result, using the combination rather than individual feature vectors is suggested for classification. In order to show the efficiency of this approach, first of all, the classification performances of the time and frequency based feature vectors in terms of overall accuracy are analyzed individually. Afterwards, the feature vectors obtained by the combination of the individual feature vectors are used in classification. The results achieved by different classifier structures are given. Obtained performances in the study are comparatively evaluated by the help of the other studies for the same dataset in advance. Results show that the combination of the features derived from cross correlation and PSD is very promising in discriminating between epileptic and healthy EEG segments.