## 4 Discussion
### Computational Cost Analysis of the Proposed Redundancy Removed DTDWT (RR-DTDWT)  
In this subsection, we analyze the computational cost of the proposed RR-DTDWT framework. In signal domain transformation part, DTDWT can be calculated with a computational cost of \(O(N)\)[61], for a EEG segment with a length of \(N\). In the energy-based feature extraction part, both mean absolute values and variance features can be calculated in \(O(N)\) computational time. Complexity-based features such as sample entropy require \(O(N^{2})\) computation time. Using the fast computation algorithms, the computation time can be reduced to \(O(N^{\frac{3}{2}})\)[62]. Moreover, considering that biomedical signals are normally saved as digitized integer-type data, the computation time of sample entropy using a fast computation algorithm is only \(O(N)\), or precisely, \(O(B^{m-1}N)\)[62], where \(B\) is the digital resolution (12 bits for Bonn database and 16 bits for CHB-MIT database) and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Paper** & **Year** & **Methods** & **Sensitivity** & **Specificity** & **FPR** & **Precision** & **Accuracy** & **F1** \\ \hline \multirow{2}{*}{[10]} & \multirow{2}{*}{2015} & Fuzzy Entropy + & \multirow{2}{*}{98.27\%} & \multirow{2}{*}{98.36\%} & \multirow{2}{*}{2.03/h} & \multirow{2}{*}{98.36\%} & \multirow{2}{*}{98.31\%} & \multirow{2}{*}{0.9831} \\  & Kolmogorov-Smirnov Test + SVM. & & & & & & & \\ \hline \multirow{3}{*}{[12]} & \multirow{3}{*}{2016} & Mel-Frequency Cepstral Coefficients & \multirow{3}{*}{98\%} & \multirow{3}{*}{96\%} & \multirow{3}{*}{4.8/h} & \multirow{3}{*}{96.08\%} & \multirow{3}{*}{97\%} & \multirow{3}{*}{0.9703} \\  & & & & & & & & \\  & & Network (ANN) Classifier. & & & & & & \\ \hline \multirow{3}{*}{[39]} & \multirow{3}{*}{2016} & Phase Space Representation + & \multirow{3}{*}{88.27\%} & \multirow{3}{*}{93.21\%} & \multirow{3}{*}{8.15/h} & \multirow{3}{*}{92.86\%} & \multirow{3}{*}{93.11\%} & \multirow{3}{*}{0.9051} \\  & & Linear Discriminant Analysis (LDA) & & & & & & \\  & & + Naive Bayesian Classifier. & & & & & \\ \hline \multirow{3}{*}{[58]} & \multirow{3}{*}{2018} & A Multi-Context Learning & \multirow{3}{*}{98.65\%} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{96.08\%} & \multirow{3}{*}{95.71\%} & \multirow{3}{*}{0.9725} \\  & & Approach by Incorporating a & & & & & & \\  & & Feature Fusion Strategy. & & & & & & \\ \hline \multirow{3}{*}{[56]} & \multirow{3}{*}{2018} & Short-Time Fourier Transform & \multirow{3}{*}{81.2\%} & \multirow{3}{*}{99.87\%} & \multirow{3}{*}{0.16/h} & \multirow{3}{*}{99.84\%} & \multirow{3}{*}{-} & \multirow{3}{*}{0.8956} \\  & & & Networks (CNN). & & & & & \\ \hline \multirow{3}{*}{[59]} & \multirow{3}{*}{2018} & Deep CNN Using 1D and 2D & \multirow{3}{*}{80.6\%} & \multirow{3}{*}{91.7\%} & \multirow{3}{*}{9.96/h} & \multirow{3}{*}{90.66\%} & \multirow{3}{*}{85.6\%} & \multirow{3}{*}{0.8534} \\  & & Spatio-Temporal Correlation & & & & & & \\  & & Features. & & & & & \\ \hline \multirow{3}{*}{[35]} & \multirow{3}{*}{2018} & Phase–Amplitude Cross-Frequency & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{97.5\%} & \multirow{3}{*}{-} \\  & & Coupling + SVM & & & & & & \\ \hline \multirow{3}{*}{[38]} & \multirow{3}{*}{2019} & Nonlinear Dynamics and Nullclines & \multirow{3}{*}{91.15\%} & \multirow{3}{*}{95.16\%} & \multirow{3}{*}{5.81/h} & \multirow{3}{*}{94.96\%} & \multirow{3}{*}{95.11\%} & \multirow{3}{*}{0.9301} \\  & & + an Ensemble of Classifiers & \multirow{3}{*}{Network.} & & & & & \\ \hline \multirow{3}{*}{[7]} & \multirow{3}{*}{2019} & PCA + Poincare Sectioning + SVM + & \multirow{3}{*}{95.7\%} & \multirow{3}{*}{96.55\%} & \multirow{3}{*}{4.14/h} & \multirow{3}{*}{96.52\%} & \multirow{3}{*}{95.63\%} & \multirow{3}{*}{0.9611} \\  & & Naive Bayes Classifier.