**4. Conclusions**

In our study, epileptic seizure detection is performed using EMD and derivative approaches, the DMD approach, which is a matrix decomposition method, and the SST approach, a new TF method. Pre-seizure and seizure EEG segments are decomposed into IMFs using the EMD and EEMD method, and time, spectral and non-linear features are calculated using the first 3 IMFs (IMF1, IMF3, IMF2) after the IMF selection process which detailed in our previous study [18]. In order to compare the success of EMD and EEMD methods, the same features are obtained using the approximation and detail coefficient of the DWT approach and directly from the EEG signal itself. While the EEMD method gives more successful results than the EMD approach for all conditions and classifiers, the most unsuccessful classification results are obtained by using features calculated from the EEG signal itself.

DMD spectra are obtained for pre-seizure and seizure EEG segments using the DMD approach, which is a simple matrix decomposition method. Although the DMD spectrum has been defined in the literature [31, 34], different features other than DMD powers have not been calculated using this spectrum. In our study, it is proposed to calculate DMD subband powers and DMD-HOS moments as features. In addition, although the multi-channel DMD approach has been used in the literature, the single-channel DMD approach has been proposed in our study. The success of the DMD approach is compared with the classical PSD obtained using the Welch method. The classification performance of both MC-DMD and SC-DMD approaches is higher than that of the PSD approach. In addition, the proposed SC-DMD based approach has been at least as successful as the MC-DMD based approach.

Another seizure detection study is carried out using the high TF resolution SST approach which proposed to overcome the disadvantages of classical TF approaches. HOJ-TF moment-based and GLCM-based features are calculated as

features using the magnitude square of SST. The same features are computed using the STFT method that is the classical TF analysis method to compare the success of SST. The SST approach provided higher classification accuracy than STFT for each condition and classifier.

EMD and EEMD approaches with high computational complexity [18], yielded more successful results than the other two approaches. As a result of these evaluations, it may be concluded that the suggested DMD and SST-based approaches that have lower computational complexity [28, 41] can successfully be used in the detection of epileptic EEG signals.
