# Wavelet-Based Multi-Class Seizure Type Classification System
## 4 Discussion  
Table 3 presents state-of-the-art techniques applied to the problem of seizure-type classification. It is difficult to compare the performance of our proposed technique with the existing studies in the literature, as each of the studies chose a different number of seizure classes. Therefore, we selected all the state-of-the-art studies considering more than three classes and compared our technique's performance with them, as presented in Table 7.  
It can be observed from Table 7 that our proposed technique's performance for specific seizure type classification is the best among all the techniques at both seizure-level classification as well as patient-level classification, achieving F1-scores of 99.1% and 74.7%, respectively. For the seven-class problem, ref. [11] reported an F1-score of 96.0% using an ensemble architecture of three DenseNets. Similarly, F1-scores of 94.5% and 90.1% were reported in [9; 26], respectively. Our proposed technique outperformed all existing studies considering the same seven classes of seizures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Specificity (\%)**} & **Sensitivity (\%)** & **Cohen’s Kappa (\%)** & **F1 Score (\%)** \\ \hline \multirow{4}{*}{**F1 Score**} & Fold 1 & 96.7 & 96.3 & 93.9 & 96.3 \\  & Fold 2 & 96.4 & 95.9 & 93.3 & 95.9 \\  & Fold 3 & 96.3 & 95.9 & 93.3 & 95.9 \\  & Fold 4 & 96.4 & 96.1 & 93.6 & 96.1 \\  & Fold 5 & 96.5 & 96.0 & 93.5 & 96.0 \\ \cline{2-5}  & Average & 96.5 & 96.0 & 93.5 & 96.0 \\ \hline \multirow{4}{*}{**F1 Score**} & Fold 1 & 97.2 & 99.1 & 97.5 & 99.1 \\  & Fold 2 & 96.8 & 99.1 & 97.5 & 99.1 \\  & Fold 3 & 97.4 & 99.2 & 97.8 & 99.2 \\  & Fold 4 & 96.6 & 98.9 & 97.0 & 98.9 \\  & Fold 5 & 96.8 & 99.1 & 97.4 & 99.1 \\ \cline{2-5}  & Average & 97.0 & 99.1 & 97.4 & 99.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Weighted average specificity, sensitivity, Cohen’s Kappa, and F1 score of the proposed method for 7-class and 5-class problems, having five folds each. Figure 9: Confusion matrix for 5-class classification problem having 5-fold cross-validation: 1 st to 5 th fold (left to right, top to bottom). For an eight-class problem, refs. [27; 28] proposed CNN-based solutions and reported an accuracy of 84.06% and 88.3%, respectively. Similarly, [PERSON] et al. [8] reported a high F1-score of 97.4% obtained by a symmetric bi-linear deep learning model consisting of two feature extractor models, CNN and LSTM. The study demonstrates a limitation in testing. The 1-second segments considered in the dataset have a 50% overlap, which always has the potential of data leaking, as mentioned by [8]. Most of the work mentioned in Table 7 is based on fast Fourier transform (FFT) [9; 11; 26], which has a high resolution in the frequency-domain but zero resolution in the time-domain, which is very essential for EEG signal processing [44]. The other approaches [8; 27; 28] were based on short-time Fourier transform (STFT), which is the known solution to overcome the limitation of FFT. STFT analyzes the frequency of the signals at a particular short time period to avoid losing temporal information. However, STFT cannot catch sharp signal events because of the use of a fixed window length and fixed basis function [44]. On the other hand, our proposed technique overcomes these shortcomings by providing a smooth representation of EEG signals. It enables generating detailed features that have strong correlations with the latent structure of seizure types in EEG signals. Additionally, our proposed method also demonstrated very high classification results compared to other classical machine learning techniques [9; 29; 30; 31].