# Wavelet-Based Multi-Class Seizure Type Classification System
## 2 Materials and Methods  
### Data  
Our study is based on TUSZ ver. 1.5.2 dataset [25], which is the largest publicly available dataset released in 2020. This dataset includes 3050 seizure events, consisting of various seizure morphologies and recorded from over 300 different patients. The TUSZ was collected from archival hospital data at Temple University Hospital (TUH), where clinical EEG data was retrieved and stored in.EDF format [25]. The signals were recorded based on the international 10-20 EEG system. Table 2 presents the details of the distribution of TUSZ. The EEG signals in TUSZ are annotated based on electrographic, electro-clinical, and clinical manifestations. More details about the dataset can be found in [25], and the dataset  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\#** & **Channels** & **\#** & **Channels** \\ \hline
1 & FP1-F7 & 2 & F7-T3 \\
3 & T3-T5 & 4 & T5-O1 \\
5 & FP2-F8 & 6 & F8-T4 \\
7 & T4-T6 & 8 & T6-O2 \\
9 & T3-C3 & 10 & C3-CZ \\
11 & CZ-C4 & 12 & C4-T4 \\
13 & FP1-F3 & 14 & F3-C3 \\
15 & C3-P3 & 16 & P3-O1 \\
17 & FP2-F4 & 18 & F4-C4 \\
19 & C4-P4 & 20 & P4-O2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: EEG channel names included in our study.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **No. of Seizure** & **Classes Considered** & **Features** & **Performance (\%)** \\ \hline Transfer learning & 8\({}^{*}\) & GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, & SFFT & 88.3 Accuracy \\ Inceptionv3 [27] & 8\({}^{*}\) & TNSZ, TCSZ, NORM & & \\ AlexNet [28] & 8\({}^{*}\) & TNSZ, TCSZ, NORM & & \\ CNN+LSTM+MLP [8] & 8 & GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, & & \\  & & TNSZ, TCSZ, MYSZ & & 97.40 F1-score \\ SeizureNet Ensemble & 7 & GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, & & \\ CNNs [11] & 7 & TNSZ, TCSZ & & 95 F1-score \\ Plastic NMN [26] & 7 & GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, & & \\  & & TNSZ, TCSZ & & 94.5 F1-score \\ K-NN [9] & 7 & GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, & & \\  & & TNSZ, TCSZ & & 90.1 F1 \\ XGBoost [9] & 7 & GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, & & \\  & & TNSZ, TCSZ & & \\ SVM [30] & 4\({}^{*}\) & GNSZ, FNSZ, TCSZ, NORM & & \\ FPGA-based ANN [31] & 3\({}^{*}\) & GNSZ, FNSZ, NORM & CWT & 95.14 Accuracy \\ SVM [29] & 4 & GNSZ, FNSZ, SPSZ, TNSZ & EMD & 95 Accuracy \\ \hline \hline \end{tabular} \({}^{*}\) Including non-seizure EEG class. +Normal EEGs.  
\end{table}
Table 3: Summary of existing state-of-the-art techniques for seizure classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\#** & **Channels** & **\#** & **Channels** \\ \hline
1 & FP1-F7 & 2 & F7-T3 \\
3 & T3-T5 & 4 & T5-O1 \\
5 & FP2-F8 & 6 & F8-T4 \\
7 & T4-T6 & 8 & T6-O2 \\
9 & T3-C3 & 10 & C3-CZ \\
11 & CZ-C4 & 12 & C4-T4 \\
13 & FP1-F3 & 14 & F3-C3 \\
15 & C3-P3 & 16 & P3-O1 \\
17 & FP2-F4 & 18 & F4-C4 \\
19 & C4-P4 & 20 & P4-O2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameters for LightGBM classifier.  
is available online at the corpus website ([[https://isip.piconepress.com/projects/tuh_eeg/](https://isip.piconepress.com/projects/tuh_eeg/)]([https://isip.piconepress.com/projects/tuh_eeg/](https://isip.piconepress.com/projects/tuh_eeg/)), accessed on 1 May 2021). The seizure type MYSZ is excluded from our study due to its scarcity in the dataset, as it is recorded from only two patients in the recently released version (see Table 2). As mentioned earlier, this decision is in accordance with previous research studies in the same field [9; 11; 26; 27; 28].