## 6 Experimental Results  
In this section, we have presented the performance evaluation of the proposed multi-scale K-NN entropy features for epileptic EEG classification. In Figures 6 and 7, the multilevel filtered signals of the seizure EEG signals (a-i) and their corresponding frequency spectrums (j-r) are presented. The distinguishing ability of the conventional K-NN entropy feature and proposed multi-scale K-NN entropy features have been tested using the confidence interval plot (99% confidence level) of the three mentioned classes (normal, seizure-free, seizure) in Figure 8a-c. In Figure 8a, the confidence interval plot of K-NN entropy features for three classes is shown. We have computed K-NN entropies of high-pass filtered EEG signals with cut-off frequency of 0.1 Hz, instead of raw EEG signals. The EEG signal samples in this database are integers, and in many cases, the Euclidean distances between nearest neighbor samples and the current sample are zero. In that case, K-NN entropies of raw EEG signals are not a finite quantity, and hence, high-pass filtered EEG signals are considered. It can be observed from Figure 8a that the confidence interval of the seizure class has a clear separation with the other two classes (seizure-free and normal), but the confidence interval of seizure-free and normal EEG signals overlaps significantly. This limits the applicability of K-NN entropy to clearly distinguish three classes. Figure 8b shows the confidence interval plot of three classes using our proposed \(\text{QEn}_{\text{LH}}\) features. It is clear from Figure 8b that the confidence intervals of seizure-free and normal classes are well separated in only the first two levels (\(\text{Recons}^{1}_{\text{LH}}\) and \(\text{Recons}^{2}_{\text{LH}}\)). We have also plotted the confidence interval of three classes using the proposed \(\text{QEn}_{\text{HL}}\) feature in Figure 8c, where all three classes are well separated in terms of their confidence intervals at each level. Thus, the proposed multi-scale K-NN entropy is robust to the dominant low-frequency component in the signal. In Table 2, the mean and SD of the proposed MSEn features, namely \(\text{QEn}_{\text{LH}}\) and \(\text{QEn}_{\text{HL}}\), are presented. This indicates that the SD computed using the proposed method for normal categories of EEG signals are smaller than expected, due to the absence of abnormalities in the signals. In Table 3, we have presented the classification performance using the conventional K-NN entropy feature computed from high-pass filtered (cut-off frequency = 0.1 Hz) EEG signals. Though we achieved reasonably good classification accuracies for most of the classification problems, the classification accuracy for seizure, seizure-free and normal (S-FN-ZO) EEG signals is 64.8%, which is significantly low. As compared to conventional K-NN entropy, the performance of the proposed multi-scale K-NN entropy features is very promising. In Table 4, we have presented the the classification performance measures using our proposed method. In our method, the parameter (\(Q\)) is varied to get better discrimination among three classes. The number of decomposition levels of TQWT are set to 8 and 16, respectively, to put forward the importance of the number of sub-bands in the proposed MSEn based classification. In most of the classification tasks, we have achieved better classification results by considering \(J=16\).