# Wavelet-Based Multi-Class Seizure Type Classification System
## 1 Introduction
### Review of Related Work
Similarly, in [28], a convolutional neural network (CNN) model, _AlexNet_, is applied to classify EEG images based on the technique of short-time Fourier transform (STFT) to classify seven types of seizure and non-seizure class. The study yielded an accuracy of 84.06%. [PERSON] et al. [8] applied a hybrid bi-linear model consisting of CNN and long short-term memory (LSTM) to classify eight types of seizures. The study reported a 97.4% F1-score. For the four-class classification of seizures, [PERSON] et al. [29] applied empirical mode decomposition (EMD) to EEGs for feature extraction and quadratic SVM for classification. The study reported an accuracy of 95%. In another study, [PERSON] et al. [30] applied EMD, Mel frequency cepstral coefficients (MFCC), and independent component analysis (ICA) to EEG data for feature extraction and SVM for classification of four classes of seizures and achieved 91.4% accuracy. For three classes of seizure classification, [PERSON] et al. [31] \begin{table}
\begin{tabular}{c c} \hline \hline
**Seizure Type** & **Seizure Description** \\ \hline FNSZ & Focal seizures which cannot be specified with its type. \\ SPSZ & Partial seizures during consciousness which is specified by clinical signs only. \\ CPSZ & Partial seizures during unconscious which is specified by clinical signs only. \\ GNSZ & Generalized seizures which cannot be further specified with its type. \\ ABSZ & Absence discharges observed on EEG where patient loses consciousness for few \\  & seconds (also known as petit mal). \\ TNSZ & Stiffening of body during seizure (EEG effects disappear). \\ TCSZ & At first stiffening and then jerking of body (also known as grand mal). \\ MYSZ & Myoclonous jerks of limbs. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Seizure type descriptions for TUH EEG Seizure Corpus (TUSZ). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Seizure Type**} & \multicolumn{3}{c}{**No. of Seizure Events**} & \multicolumn{3}{c}{**Duration (s)**} & \multicolumn{3}{c}{**No. of Patients**} \\ \cline{2-7}  & **ver.1.4.0** & **ver.1.5.2** & **ver.1.4.0** & **ver.1.5.2** & **ver.1.4.0** & **ver.1.5.2** \\ \hline FNSZ & 992 & 1836 & 73,466 & 121,139 & 109 & 150 \\ GNSZ & 415 & 583 & 34,348 & 59,717 & 44 & 81 \\ CPSZ & 342 & 367 & 33,088 & 36,321 & 34 & 41 \\ ABSZ & 99 & 99 & 852 & 852 & 13 & 12 \\ TNSZ & 67 & 62 & 1271 & 1204 & 2 & 3 \\ TCSZ & 50 & 48 & 5630 & 5548 & 11 & 12 \\ SPSZ & 44 & 52 & 1534 & 2146 & 2 & 3 \\ MYSZ & 3 & 3 & 1312 & 1312 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data distribution for different types of seizures in two versions of TUSZ. developed a field programmable gate array (FPGA)-based framework for the classification of generalized and focal epileptic seizures using a feed-forward multi-layer neural network and achieved an accuracy of 95.14%. In spite of the good performance reported in the aforementioned studies, it is expected that the reported techniques cannot be used in real world situations as the studies either did not report the performance when tested on data from new patients or reported lower performance. Out of the eight studies presented in Table 3, only two studies [9; 11] considered the generalization of their proposed techniques. Both studies mentioned a considerable decrease in the performance of their system, where the performance decreased by 45%. This shows that there is still a large gap for advancement for better generalization capability for the classification systems. It is interesting to observe from Table 3 that the authors of these studies chose a different number of seizure classes, ranging from a three-class problem to an eight-class problem for seizure type classification. The reason behind the choice of the number of classes is not discussed in most of these studies. The authors of [9; 11; 26; 27; 28] excluded the seizure type MYSZ from their experiments because the signals of this type were only recorded from two patients (see Table 2).