## 5 Conclusions and Future Works  
The goal of this paper was to attempt to solve the nonlinear and non-stationary problems of analyzing EEG signals to achieve recognition of healthy, epileptic seizure-free, and epileptic seizure patients. For this reason, an automated epileptic seizure detection method was constructed. The idea was sourced from a novel and reasonable hypothesis that mimics the generation mechanism of EEG signals to some sense. From the novel hypothesis, the characteristics of EEG signals depended on the construction of the brain cells and these EEG signals involved the macroscopic performance of a large number of neurons. This means that the EEG signals should be first decomposed into several subcomponents that describe the different states of brain, from low frequency to high frequency, which can be employed to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Authors** & **Methods** & **Tasks** & **Aver.** \\ \hline \multirow{2}{*}{2014} & \multirow{2}{*}{[PERSON] et al. [14]} & Discrete wavelet transform (DWT) & A-E, B-E, C-E, & 100.0, 100.0, 99.60, \\  & & Fuzzy approximate entropy (fApEn) & D-E, ACD-E, & 95.85, 98.15, \\  & & Support vector machines (SVMs) & BCD-E, ABCD-E & 98.22, 97.38 \\ \hline \multirow{2}{*}{2015} & \multirow{2}{*}{[PERSON] et al. [7]} & \multirow{2}{*}{rational functions (RFs)} & A-E, B-E, C-E, & 99.80, 99.30, 98.50, \\  & & D-E, A, B, C, D-E & 94.90, 98.10 \\ \hline \multirow{2}{*}{2016} & \multirow{2}{*}{[PERSON] et al. [11]} & Discrete Wavelet Transforms (DWT) & A-E, B-E, C-E, & 99.97, 99.92, 99.82, \\  & & Phase Space Reconstruction (PSR) & D-E, ABCD-E, & 99.76, 99.38, \\  & & Singular values of covariance matrix & AB-CD-E, CD-E & 99.53, 98.89 \\ \hline \multirow{2}{*}{2017} & \multirow{2}{*}{[PERSON] et al. [22]} & local mean decomposition (LMD) & A-E, De, ABCD-E, & 100.09, 98.87, \\  & & BPNN, KNN, LDA, SVM, etc. & A-D-E, AB-CD-E & 98.47, 98.40 \\ \hline \multirow{2}{*}{2017} & \multirow{2}{*}{[PERSON] et al. [26]} & Discrete Wavelet transformation (DWT) & A-E, B-E, C-E, & 100.0, 98.00, 99.00, \\  & & Correlation-based Feature Selection (CFS) & D-E, ACD-E, & 98.50, 98.50, \\  & & Improved Correlation-based Feature & BCD-E, CD-E, & 97.50, 98.67, \\  & & Random Forest (RF) & ABCD-E & 97.40 \\ \hline \multirow{2}{*}{2018} & \multirow{2}{*}{**This paper**} & **The novel hypothesis** & **A-E, B-E, C-E,** & **100.0, 100.0, 99.86,** \\  & & **LMD, MFCC** & **D-E, ABCD-E,** & **99.78, 99.46,** \\  & & **BPNN, KNN, LDA** & **AB-CD-E, CD-E,** & **97.85, 99.83,** \\  & & **SVM, DT** & **A-B-C-D-E** & **97.43** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The comparison between this method and other methods using the same database. demonstrate the active or non-active areas of neurons. A local mean decomposition algorithm was able to attain this goal, and can be considered as the solution to the non-stationary problem. The problem then becomes how to separate these subcomponents from active or non-active areas of neurons into the feature vectors, which can be used to achieve EEG segment recognition. The Mel-frequency cepstrum coefficient (MFCC) was used to achieve the deconvolution calculation to separate these various subcomponents, and can be considered as the solution to the nonlinear problem. In future works, the suitable length of the window overlap to build a new EEG segment should be studied by determining the impacts of the length of the signal acquisition changes on the amount of information. Secondly, we hope to evaluate our proposed method for automatically analyzing long recording EEG signals. Thirdly, we will apply this method to solving the problem of classifying multichannel sleep EEG signals and the recognition of different individual behaviors using EEG signals. Furthermore, ultrasound and optoacoustics, applied in epileptic seizure detection, can be considered as the recent advances in this field, which have been reported in mainstream journals [17; 18; 19; 20; 21; 22; 23; 24].