# Classification of EEG Signals for Prediction of Epileptic Seizures
## 7 Results and Discussion
In this iteration, the same settings were used as in the first iteration, with the only difference being the use of VGG-16 in place of Resnet. Similarly, ANN was used for classification. The results improved drastically with this change, and sensitivity of 87.3% and specificity of 83.2% were achieved. The results obtained using this setting were on par with the state-of-the-art systems, but to further enhance and analyze the effects of different feature extraction models, we designed a custom convolutional neural network with fewer layers. Similar architectures were also found in the literature after thorough study. The details of this network are explained in the Proposed Method section. The best results were achieved with this network, where a sensitivity of 89.3% and a specificity of 85.2% was achieved. This network was kept constant along with the preprocessing in the subsequent experiment. Table 4 shows the settings and the results achieved in each iteration in tabular form. The third experiment was devised to analyze the efficiency of different classifiers. In this experiment, we fixed the two best settings from the previous experiments, i.e., non-overlapping window for the interictal class and overlapping window of 15 s for the preictal class, with STFT and bandpass/bandstop filtering and the custom CNN for feature extraction. In the first iteration, we used ANN, which is similar to the third iteration of Experiment 2. In the second iteration, we used decision tree and achieved sensitivity of 81.2% and specificity of 76.4%. In the third iteration, support vector machine was used after feature extraction. The results obtained by application of LSTM for classification were sensitivity of 92.8% and specificity of 90.7%. Table 5 shows the settings and the results achieved in each iteration in tabular form. Different deep learning approaches were tried for automated feature extraction, including Resnet-101, Resnet-50, and VGG-16. Table 6 gives a comparison of these approaches with respect to sensitivity and specificity. Preprocessing was kept the same for all three experiment, with a non-overlapping window of 30 s for the interictal state and a 15 s overlapping window for the preictal state and PREP pipeline to remove the noise; \(k\)-fold cross-validation was applied for validation of results by keeping the value of \(k=10\). The proposed system achieved an average accuracy of 94%, 93.8% sensitivity, 91.2% specificity with standard deviation ranges between 1 to 1.5% for \(k\) folds in all performance measures. Table 7 shows the results of \(k\)-fold cross-validation achieved from the proposed method. An average prediction time of 19.5 min was achieved. Figure 5 and Table 8 compare the results of the proposed system with the current state-of-the-art systems. [PERSON] et al. [52] achieved better results in terms of accuracy; however, they did not reported the average anticipation time, which is of prime importance in epilepsy prediction systems, as achieving greater accuracy with less time to control the seizure limits the usefulness of that method. Therefore, the proposed system performs better both in terms of specificity and sensitivity. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Preprocessing** & **Feature Extraction** & **Classification** & **Sensitivity** & **Specificity** \\ \hline NOW interictal & & & & \\ OW preictal (15 s overlap) STFT & Resnet-50 & ANN & 67.4 & 54.3 \\ BPF/BSF & & & & \\ \hline NOW interictal & & & & \\ OW preictal (15 s overlap) STFT & VGG-16 & ANN & 87.3 & 83.2 \\ BPF/BSF & & & & \\ \hline NOW interictal & & & & \\ OW preictal (15 s overlap) STFT & Custom-CNN & ANN & 89.3 & 85.2 \\ BPF/BSF & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of various feature extraction techniques used in the proposed system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Extraction** & **Sensitivity** & **Specificity** \\ \hline Resnet-101 & 68.4 & 59.6 \\ \hline Resnet 50 & 78.4 & 71.3 \\ \hline VGG-16 & 93.8 & 91.2 \\ \hline \hline \end{tabular}
\end{table}