# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 4 Discussion
### Comparisons with Previous Methods  
A wide range of algorithms have been proposed in the literature for the automatic detection of epileptic seizures, leveraging the Bonn EEG database. Many studies categorize two or three distinct states, such as normal, inter-ictal, and ictal states. The proposed method is evaluated against existing approaches on the same benchmark dataset, as demonstrated in Table 3.  
As can be seen by the above results, the proposed method outperforms most comparative methods. For binary classification problems, classification of healthy volunteers and seizures is done using different dataset combinations from A and B with E (AB vs. E). [PERSON] et al. proposed a novel 1D-CNN for robust detection of seizures, which could reach 99.38% recognition accuracy. [PERSON] and [PERSON] proposed the framework based on a discrete wavelet transform (DWT) analysis and KNN, which achieved an accuracy of 99.16%. The method of epileptic detection with Neo-Natal EEG signals was proposed based on the LSTM classifier, which could reach 99.17% recognition accuracy. The proposed model was recognized with an accuracy of 99.94%. In particular, both sets D and E are difficult to distinguish because the two datasets are from the epileptogenic zone. The recognition rate of [PERSON]'s method of 1D-CNN could reach 98.02% accuracy. [PERSON] et al. achieved 99.50% recognition accuracy with tunable-Q wavelet transform (TQWT) and SVM. [PERSON] et al. proposed a novel epileptic recognition method combining wavelet packet decomposition (WPD) and KNN, and the highest mean accuracy of 99.36% was obtained. The epileptic recognition rate of the proposed method could achieve 99.49%. Similarly, it \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Category** & **Methods** & **Auther** & **Acc (\%)** & **Our Acc (\%)** \\ \hline \multirow{4}{*}{AB vs. E} & 1D - CNN & [PERSON] et al. [36] & 99.38\% & \\  & DWT + KNN & [PERSON] et al. [37] & 99.16\% & 99.94\% \\  & LSTM & [PERSON] et al. [38] & 99.17\% & \\  & 1D - CNN & [PERSON] et al. [36] & 98.02\% & \\ C vs. E & TQWT + SVM & [PERSON] et al. [39] & 99.50\% & 99.49\% \\  & WPD + KNN & [PERSON] et al. [40] & 99.36\% & \\  & 1D - CNN & [PERSON] et al. [36] & 97.63\% & \\ D vs. E & CNN + SVM & [PERSON] et al. [39] & 98.00\% & 99.62\% \\  & WPD + KNN & [PERSON] et al. [40] & 98.36\% & \\  & 1D - CNN & [PERSON] et al. [36] & 96.73\% & \\  & LSSVM & [PERSON] et al. [41] & 97.19\% & \\  & CWT + CNN & Türk and Omer [42] & 97.00\% & \\  & LSTM & [PERSON] et al. [38] & 97.00\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the proposed method and existing methods using the same datasets. is difficult to detect epilepsy in sets D and E from the epileptogenic zone. The epileptic recognition rate of [PERSON]'s, [PERSON]'s, and [PERSON]'s studies can reach 97.63%, 98.00%, and 98.36%, respectively. The recognition rate of 99.62% is attained by using the proposed method. For the three-category problem, the recognition rate of [PERSON]'s method can be up to 96.73%. [PERSON] et al. proposed a novel method with a least-squares support vector machine, and the epileptic recognition accuracy showed a mean accuracy of 97.19%. [PERSON] et al. proposed a new convolutional neural network structure used to learn the properties of these scalogram images, and the classification performance could reach 97.00%. The epilepsy detection method with Neo-Natal EEG was proposed based on the LSTM classifier, which could get 94.81% recognition accuracy. Our proposed model achieved a mean accuracy of 99.06%. The recognition rate for dichotomous and trichotomous epileptic types is approximately higher than existing methods, which effectively improves the recognition rate of epileptic detection.