## 5 Results  
This paper carried out an extensive and comprehensive analysis of various feature extraction, detection, and classification techniques to classify focal and non-focal EEG signals that help doctors in the localization of the epileptogenic zone for surgical operations. Moreover, the paper proposed an efficient and robust feature extraction scheme based on statistical, time-frequency and nonlinear features. The adopted hybrid techniques consist of various features combined with different SVM approaches and several deep neural network structures. Focal and non-focal EEG datasets were acquired and downloaded from the Bern-Barcelona database. Each focal and non-focal EEG signal consists of a pair of signals corresponding to two channels denoted as x and y. In this work, we adopted the approach employed in [52] of computing the average value of features calculated from the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title 1** & **Name** & **Theory** \\ \hline \multirow{4}{*}{Entropy Features} & [PERSON] & \(En_{stan}=\sum_{n=1}^{x}S_{n}\log_{2}S_{n}\) \\ \cline{2-3}  & [PERSON] & \(En_{repair}=\frac{1}{1-\frac{1}{n}}\sum_{n=1}^{x}\log(S_{n})^{x}\) \\ \cline{2-3}  & Log Energy & \(En_{logn}=\sum_{n=1}^{x}\log(s_{n}^{2})\) \\ \cline{2-3}  & Permutation & \(En_{per}=-\sum_{n=1}^{x}S_{n}\log_{2}S_{n}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Nonlinear Entropies Parameters Computed in this study.  
Figure 9: Description of 10-fold cross-validation.  
EEG rhythm. For this, 7500 EEG records were randomly selected for each signal to form the complete EEG records for training and testing as a.txt file. The dataset was divided into training and test sets with 70% and 30% standards for training and validation, respectively, in the SVM classifier models. While, for the DNN models, the dataset was divided into 80% and 20% for training and testing, respectively. Out of the 80% assigned for training, the standard 70% and 30% were used for training and validation. For preprocessing and feature extraction, a MATLAB environment was utilized for those operations, while an efficient DNN library that runs on top of TensorFlow, known as Keras, was employed during the DNN modelling process. For each training cycle, 100 was chosen as the batch size. A 10-fold cross-validation was used in training and test instances.  
Features from different domains have been tested using a \(t\)-test in SPSS software to obtain significant features. After performing the test, 78 features were deployed as hybrid features because their \(p\)-value was less than 0.05, which means that they are statistically significant. Some features, such as dimensional fractal features, were discarded in the analysis because their \(p\)-value was higher than 0.05. For the WT-SST time-frequency domain, 16 features in the FC4 layer were found to be statistically significant as their p-value was less than 0.05. The proposed 1D-CNN was used to classify the raw EEG signals and DWT features, while the time-frequency matrix of focal and non-focal EEG signals resulting from WT-SST was used in the classification with 2D-DCNN architecture, which extracts learnable feature maps.  
The classifier's performance is evaluated using the following performance parameters:  
\[\text{Sensitivity} = \frac{\text{TP}}{\text{TP}+\text{FN}}\] \[\text{Specificity} = \frac{\text{TN}}{\text{TN}+\text{FP}}\] \[\text{Accuracy} = \frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+\text{ TN}}\] \[\text{Precision} = \frac{\text{TP}}{\text{TP}+\text{FP}}\] \[\text{F1\_Score} = 2\frac{\text{Precision}+\text{Sensitivity}}{\text{Precision}+ \text{Sensitivity}}\]  
where TP is True Positive (correctly identified), FP is False Positive (incorrectly identified), FN is False Negative (incorrectly rejected), TN is True Negative (correctly rejected).