## 7 Discussion  
The performance of the proposed multi-scale K-NN entropy is enhanced using the TQWT-based [43] multilevel filtering approach. The multi-level filtering based on TQWT has some major benefits. For analyzing signals of an oscillatory nature, a higher value of the \(Q\) factor is well suited. This is because with the increased value of the \(Q\) factor, the generated wavelets will be more oscillatory with increased center frequency and relatively narrow frequency responses. On the other hand, for a lower value of \(Q\) factors, the generated wavelets are less oscillatory and posses a wider frequency response as compared to their center frequencies. Thus, a lower value of the \(Q\) factor may be chosen for non-oscillatory transient signals [44]. These properties of the \(Q\) factor motivated us to use TQWT in our multi-scale analysis. In this work, the multi-scaling operation is carried out starting from the low-frequency component and gradually introducing high frequency components cumulatively in successive scales. This is useful when analyzed signals have dominant high-frequency components. In multi-scaling with a lower scale factor, the reconstructed signal components after accumulation do not posses high-frequency components. Thus, with a lower scale factor, the complexity of low-frequency components can be computed. We have also performed the multi-scaling operation in the reverse direction, starting from the very high-frequency component signal and added low-frequency components in successive scales. This enables us to analyze high-frequency components of the signals without including the low-frequency component, which is predominant. As a result, the use of TQWT-based multilevel filtering enhances the discriminating ability of the conventional K-NN entropy features. In the previous studies, the TQWT framework together with K-NN entropy [9] and spectral features [10] have been proposed for epileptic EEG classification. The proposed method in this paper presents a generalized form of K-NN entropy in the TQWT framework, which has also been studied for the classification of seizure and seizure-free EEG signals [9]. Moreover, the proposed entropy feature has been applied for multiple classification problems associated with epileptic EEG signals. The proposed method also incorporates the feature selection technique before classifying EEG signals using the SVM classifier. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Filtering Type**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Experiment Type**} & \multicolumn{3}{c}{**Number of Levels \(J=8\)**} & \multicolumn{3}{c}{**Number of Levels \(J=16\)**} \\ \cline{3-8}  & & & **Acc (\%)** & **Sens (\%)** & **Spec (\%)** & **Acc (\%)** & **Sens (\%)** & **Spec (\%)** \\ \hline \multirow{8}{*}{\(Q=1;R=3\)} & S-Z & **100** & 100 & 100 & **100** & 100 & 100 \\  & S-O & 99.5 & 99 & 100 & 99.5 & 99 & 100 \\  & S-N & 98.5 & 98 & 99 & 99 & 99 & 99 \\  & S-F & 97.5 & 97 & 98 & 97.5 & 97 & 98 \\  & S-FNZO & 98.4 & 95 & 99.3 & 98.8 & 95 & 99.8 \\ \cline{2-8}  & S-FN-ZO & 97.2 & 95.5 & 98.66 & 97.2 & 95.5 & 98.67 \\  & & & 100 & 97.66 & & 100 & 98 \\ \hline \multirow{8}{*}{**Detail to approximation**} & S-Z & 98.5 & 98 & 99 & **100** & 100 & 100 \\  & S-O & 95.5 & 94 & 97 & **100** & 100 & 100 \\  & S-N & 99.5 & 100 & 99 & **99.5** & 99 & 100 \\  & S-F & 97 & 96 & 98 & 97.5 & 97 & 98 \\  & S-FNZO & 98.2 & 92 & 99.8 & 98.8 & 96 & 99.5 \\ \cline{2-8}  & & & 94 & 99.75 & & 96 & 99.75 \\  & S-FN-ZO & 92.2 & 86.5 & 98.33 & **98.6** & 98.5 & 98.67 \\  & & & 97 & 89 & & 100 & 99.33 \\ \hline \multirow{8}{*}{\(Q=3;R=3\)} & S-Z & 97 & 95 & 99 & 98.5 & 98 & 99 \\  & S-O & 96 & 92 & 100 & 97 & 94 & 100 \\ \cline{1-1}  & S-N & 99.5 & 99 & 100 & **99.5** & 99 & 100 \\ \cline{1-1}  & S-F & 97 & 96 & 98 & 98 & 98 & 98 \\ \cline{1-1}  & S-FNZO & 98.2 & 91 & 100 & 98.8 & 96 & 99.5 \\ \cline{1-1} \cline{2-8}  & S-FN-ZO & 92.2 & 88.5 & 98.33 & 97.4 & 96 & 99 \\ \cline{1-1}  & & & 97.5 & 88.66 & & 99.5 & 97.33 \\ \hline \hline \end{tabular}
\end{table}