## 4 Discussion  
This study addressed the problem of seizure vs. seizure-free EEG record classification. The classifier design and validation used data from 341 subjects in the TUSZ database, a larger amount of data than previously used. Several studies have highlighted the advantage of complexity measures as EEG signal description in seizure detection [2; 4; 5; 9; 10; 11; 13; 15; 19; 20], and others showed the importance, as well, of signal power in different frequency bands [3; 7; 8; 19]. We inquired into the performance of these features with the larger TUSZ database to gain some understanding of the effect of the experimentation database size on seizure detection potency. We followed this with the study of a new method that exploits both types of features. We investigated different feature-grouping options, followed by machine-learning classification of selected features to identify seizure segments. Two feature subsets of prevailing features, as well as their combination, were investigated to evaluate their seizure detection potency. In contrast to earlier studies that focused on patient-specific seizure detection, this study addressed patient-independent detection, using data from 341 subjects. Signal complexity and power both yielded good classification, with signal power providing slightly better results. When used jointly, signal complexity and power gave superior performance: 95% AUC with RF classification, 94% with GBDT, 86% with SVM, and 88% with k-NN. RF outperformed other classifiers systematically in all experiments. This may be explained by the fact that decisions made by the RF classifier correspond to unpruned and diverse trees that lead to high resolution in feature space. Random operations in the training and voting procedures of RF also contribute to better classification by addressing the issue of overfitting [50]. This study showed the classification significance of the features at the channel level. Unlike most others, which limited features to one channel (e.g., BONN dataset) or six channels (e.g., Freiburg dataset), the TUSZ database used in validating the method in this study has more than 19 channels corresponding to the 10-20 standard system. This allowed us to investigate the performance of the method using only one channel at each run. Results showed that a maximum distinction between seizure and seizure-free records was obtained from the Pz channel, with a corresponding AUC of 94%. In addition, the method results show that the parietal and frontal-parietal regions were effective in extracting features that can discriminate seizure from seizure-free records, wherein the performance for each channel obtained was above 90%. Additionally, this study addressed the effect of adding a feature selection step prior to classification. The analysis removed the less informative  
Figure 5: Box plots corresponding to the accuracy of the classification of the two classes of seizure and seizure-free. The orange line in the boxes marks the median. The final accuracy score is determined by averaging the 10 ROC curves and calculating the AUC of the mean ROC. features using the \(p\)-value test: If the \(p\)-value was greater than 0.001, the feature extracted from a specified channel was discarded from the group considered. Only a slight decrease in performance occurred for RF and GBDT with feature selection, keeping in mind that the method is computationally more efficient when running on a smaller set of features. The results are consistent with findings in a previous study [51] that compared random forest variable selection methods for classification. Indeed, the latter concluded that there was practically no difference in prediction error rates between all methods. However, it was obvious that SVM was more sensitive to the number of features, with a decrease in accuracy rate as the number of features increased.