# Wavelet-Based Multi-Class Seizure Type Classification System
## 4 Discussion
Moreover, all of the research studies mentioned in Table 7 utilized an older version of the TUSZ, which is ver.1.4.0, whereas the number of seizure events in the current version is much larger as compared to the previous version, as shown in Table 2. With a more challenging new version of TUSZ ver.1.5.2, which contains 1000 additional seizure events, our proposed method achieves better results for the seven-class classification problem as compared to existing techniques. Most of the research studies in the literature chose to evaluate their methods only at the seizure level. Out of the eight studies presented in Table 3, only [9; 11] considered the generalization of their models over different patients, or, in other words, the model is trained and evaluated on data from different patients. This ensures that the performance of the model is general and can be adopted for different patients. The performance of both studies [9; 11] sharply decreases when evaluated using the patient-wise cross-validation technique, as shown in Table 7. Comparatively, our proposed method showed a competitive result for the seven-class problem, and it demonstrated more stable performance when evaluated across different patients for the five-class problem. Our proposed technique achieved F1-scores of 56.22% and 74.7% for the classification of the seven-class and five-class problems, respectively. Regarding [23], DTCWT was employed to extract features from EEGs to classify epileptic vs. non-epileptic patients; however, in this study, we are using the DTCWT with a different set of features for a more complex problem, which is the identification of the \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **No. of Seizure Types** & **Seizure\_Wise CV (\%)** & **Patient\_Wise CV (\%)** \\ \hline SVM [29] & 4 & 95.00 \({}_{\text{Acc}}\) & — \\ SVM [30] & 4 \({}^{\star}\) & 95.14 \({}_{\text{Acc}}\) & — \\ SeizureNet [11] & 7 & 95.00 \({}_{\text{H}}\) & 62 \(n\) \\ KNN [9] & 7 & 90.1 \({}_{\text{H}}\) & 40.1 \({}_{\text{H}}\) \\ XGBoost [9] & 7 & 85.1 \(n\) & 54.2 \(n\) \\ SGD [9] & 7 & 80.7 \(n\) & 46.9 \(n\) \\ CNN [9] & 7 & 71.8 \(n\) & 52.5 \(n\) \\ NMN [26] & 7 & 94.5 \(n\) & — \\ Inceptionv3 [27] & 8 \({}^{\star}\) & 88.3 \({}_{\text{Acc}}\) & — \\ AlexNet [28] & 8 \({}^{\star}\) & 84.06 \({}_{\text{Acc}}\) & — \\ CNN+LSTM [8] & 8 & 97.40 \(n\) & — \\ This Work & 5 & 99.1 \(n\) & 74.7 \(n\) \\ This Work & 7 & 96.04 \(n\) & 56.22 \(n\) \\ \hline \hline \end{tabular} \({}^{\star}\)Including non-seizure EEG class.  
\end{table} 
Table 7: Patient-wise cross-validation and seizure-wise cross-validation results for studies found in the literature. seizure types, including specific and non-specific focal and generalized seizures. Moreover, we evaluated this technique seizure-wise and patient-wise on the most extensive available EEG dataset, TUSZ ver.1.5.2 [25], containing data from more than 300 patients. In [23], the data used for evaluation were obtained from only 21 subjects. This study has explored the generalization of our technique to be evaluated across different patients for better generalization capability. In addition to the evaluation, we speculated that when only considering the EEG signals, it is not appropriate to treat the main seizure categories as unique seizure types against any of their sub-types. Since both SPSZ and CPSZ are sub-categories of focal seizures, it is unreasonable to train the machine learning algorithm to differentiate between them. According to the dataset, the reason for labeling an event as a focal non-specific seizure is the lack of information to make a decision [25]. After excluding the non-specific seizure labels, the experimental results demonstrated the stability of the classifier across different patients, as shown in Table 7. The performance of our proposed technique for specific seizure type classification showed nearly perfect results when evaluated at the seizure level, and it has the ability to generalize itself better on signals recorded from new patients, as compared to [9; 11].