## 4 Results and Discussion
### Recognition Result  
In this study, a total of 6368 EEG segments were considered, and can be considered as the most difficult recognition problem. Systematic analysis seems highly necessary, and accordingly the experiment was conducted by considering all the classification problems, using the four cases, which completely contained all the possible situations. In order to evaluate the performance of this method with the four cases, Table 2 illustrates the classification results with the five classifiers. Based on the results, the highest in Case 1 was 100%, which means that the combination, created with the characteristics as the key features in all the experiments, can be employed to obtain a perfect recognition result. Figure 5: The first series of Mel-frequency cepstrum coefficients (MFCCs) in product function1 (PF1) of five sets (sets A, B, C, D, and E), for Case 1 to Case 4 from top-down. Figure 6: The first series of Mel-frequency cepstrum coefficients (MFCCs) of five sets (sets A, B, C, D, and E) in Case 4, for product function1 (PF1) to product function4 (PF4) from top-down. The results in Table 2 suggest that Case 1 (healthy versus epileptic seizure) yielded a high accuracy by this method. In order to assess the performance of this proposed method, other cases were also used and classified, as shown in Tables 3-6. The results can be used to provide enough evidence to show the efficiency of this method on extracting the useful features. The results in Table 3 demonstrate the detection ability for healthy EEG groups versus epileptic seizure groups, whereas Table 4 illustrates the results of the epileptic seizure-free versus epileptic seizure groups. Table 5 describes the three classification problem for healthy, epileptic seizure-free, and epileptic seizure EEG segments. Table 7 tries to explain all the differences between the five EEG datasets. It should be noted that all of the classification problems were solved perfectly. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sets** & **Class** & **BPNN** & **SVM** & **LDA** & **KNN** & **DT** & **Aver.** \\ \hline \multirow{4}{*}{A} & Healthy-A & **89.38** & **97.05** & **82.98** & **92.09** & **93.28** & **90.06** \\  & Healthy-B & 9.36 & 2.95 & 15.64 & 4.21 & 6.22 & 7.680 \\  & Inter-ictal-C & 0.19 & 0.00 & 0.44 & 2.45 & 0.19 & 0.636 \\  & Inter-ictal-D & 0.94 & 0.00 & 0.94 & 0.82 & 0.31 & 0.602 \\  & Epileptic-E & 0.13 & 0.00 & 0.00 & 0.44 & 0.00 & 0.114 \\ \hline \multirow{4}{*}{B} & Healthy-A & 11.68 & 3.45 & 12.63 & 4.15 & 5.53 & 7.488 \\  & Healthy-B & **87.25** & **96.55** & **87.00** & **94.91** & **94.28** & **92.00** \\  & Inter-ictal-C & 0.38 & 0.00 & 0.06 & 0.44 & 0.13 & 0.202 \\  & Inter-ictal-D & 0.57 & 0.00 & 0.31 & 0.19 & 0.06 & 0.226 \\  & Epileptic-E & 0.13 & 0.00 & 0.00 & 0.31 & 0.00 & 0.088 \\ \hline \multirow{4}{*}{C} & Healthy-A & 0.63 & 0.06 & 0.38 & 0.00 & 0.19 & 0.252 \\  & Healthy-B & 0.32 & 0.06 & 0.00 & 0.00 & 0.31 & 0.138 \\  & Inter-ictal-C & **98.43** & **99.81** & **99.37** & **100.0** & **99.43** & **99.41** \\  & Inter-ictal-D & 0.57 & 0.06 & 0.25 & 0.00 & 0.06 & 0.188 \\  & Epileptic-E & 0.06 & 0.00 & 0.00 & 0.00 & 0.00 & 0.012 \\ \hline \multirow{4}{*}{D} & Healthy-A & 0.06 & 0.06 & 0.06 & 0.00 & 0.06 & 0.048 \\  & Healthy-B & 0.25 & 0.00 & 0.00 & 0.00 & 0.25 & 0.100 \\  & Inter-ictal-C & 0.06 & 0.00 & 0.00 & 0.00 & 0.00 & 0.012 \\ \hline \multirow{4}{*}{E} & Healthy-A & 0.06 & 0.06 & 0.06 & 0.00 & 0.06 & 0.048 \\  & Healthy-B & 0.25 & 0.00 & 0.00 & 0.00 & 0.25 & 0.100 \\  & Inter-ictal-C & 0.06 & 0.00 & 0.00 & 0.00 & 0.00 & 0.012 \\ \hline \end{tabular}
\end{table} 
Table 6: Classification results of the five classifiers on all five data-sets of Case 4 (Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K-nearest neighbour (KNN), Decision Tree (DT), average value (Aver. )).