## 3 Results
### Performance of Group of Features Extracted from All Channels with Feature Selection  
For this study, we executed three sets of experiments to investigate the effect of the recommended thresholds. Consistent with previous findings [48; 49], we found that reducing the features by the criteria that their \(p\)-values be less than 0.001 led to improved performance. All features with a \(p\)-value higher than 0.001 were discarded from the feature vector as non-descriptive. Classification performance was evaluated using AUC, F-measure, and accuracy. Table 6 displays the performance of the three sets of features. RF was able to achieve an accuracy of 90.30% and an F-measure of 90.76%, with a decrease of 1%, compared to the experiments done without the feature selection step. There was no significant change in the performance of the three classifiers RF, GBDT, and k-NN between the experiments done with and without univariate feature selection. SVM accuracy increased from 78.72% to 81.92%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Band**} & \multicolumn{2}{c}{**Seizure-Free**} & \multicolumn{2}{c}{**Seizure**} \\  & & **(Mean \(\pm\) Std in mV)** & **(Mean \(\pm\) Std in mV)** & **\(p\)-Value** \\ \hline Absolute & Delta & 0.89 \(\pm\) 0.02 & 0.87 \(\pm\) 0.03 & 5.694 \(\times\) 10\({}^{-124}\) \\ power & Theta & 0.89 \(\pm\) 0.02 & 0.87 \(\pm\) 0.03 & 1.156 \(\times\) 10\({}^{-129}\) \\  & Alpha & 0.89 \(\pm\) 0.02 & 0.87 \(\pm\) 0.03 & 7.125 \(\times\) 10\({}^{-133}\) \\  & Beta & 0.90 \(\pm\) 0.02 & 0.87 \(\pm\) 0.04 & 9.067 \(\times\) 10\({}^{-133}\) \\  & Gamma & 0.90 \(\pm\) 0.02 & 0.87 \(\pm\) 0.04 & 3.423 \(\times\) 10\({}^{-133}\) \\ \hline Relative & Delta & 0.88 \(\pm\) 0.02 & 0.86 \(\pm\) 0.03 & 8.007 \(\times\) 10\({}^{-132}\) \\ power & Theta & 0.88 \(\pm\) 0.02 & 0.53 \(\pm\) 0.04 & 9.594 \(\times\) 10\({}^{-128}\) \\  & Alpha & 0.25 \(\pm\) 0.16 & 0.15 \(\pm\) 0.11 & 7.173 \(\times\) 10\({}^{-145}\) \\  & Beta & 0.26 \(\pm\) 0.15 & 0.16 \(\pm\) 0.11 & 1.367 \(\times\) 10\({}^{-129}\) \\  & Gamma & 0.27 \(\pm\) 0.14 & 0.19 \(\pm\) 0.11 & 1.344 \(\times\) 10\({}^{-93}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Range of values of the spectral power measures for the two classes of seizure and seizure-free. The lowest \(p\)-value of 7.173 \(\times\) 10\({}^{-145}\) for the relative power at the alpha band indicates the great discrimination capability of this feature in characterizing seizure records.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Classifiers** & & **RF** & & **GBDT** & & **SVM** & & & **K-NN** & \\ \hline
**Features** & **AUC\%** & **F\%** & **ACC\%** & **AUC\%** & **F\%** & **ACC\%** & **AUC\%** & **F\%** & **ACC\%** & **AUC\%** & **F\%** & **ACC\%** \\ \hline Complexity & 95 \(\pm\) 01 & 91.05 & 90.68 & 93 \(\pm\) 01 & 89.86 & 89.48 & 79 \(\pm\) 03 & 71.44 & 73.57 & 85 \(\pm\) 02 & 88.92 & 87.90 \\ \hline Oscillatory & power & 95 \(\pm\) 01 & 91.33 & 90.95 & 94 \(\pm\) 01 & 90.68 & 90.40 & 88 \(\pm\) 02 & 79.29 & 80.65 & 89 \(\pm\) 02 & 89.67 & 88.73 \\ \hline Complexity & 95 \(\pm\) 01 & 91.41 & 91.07 & 94 \(\pm\) 01 & 90.95 & 90.67 & 86 \(\pm\) 02 & 77.24 & 78.72 & 88 \(\pm\) 02 & 90.09 & 89.16 \\ and power & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification performance for the complexity measures, oscillatory power, and a combination of the two without the univariate feature selection.