## 4 Discussion
### Comparison with Previous Studies
#### 4.1.2 Comparison with Previous Studies Based on CHB-MIT Database  
For the CHB-MIT database, a method comparison is shown in Table 6. Different studies based on CHB-MIT database employed a wide variety of metric combinations to evaluate algorithm performance. For example, study [10; 56] applied specificity and false positive rate (FPR) to characterize model performance respectively. To compare all methods consistently, we converted the two metrics into each other. For the proposed method, a specificity of 99.63% is equivalent to a false positive rate of 0.44/hour, under the assumption of 30 s EEG segments. The proposed RR-DTDWT can achieve a higher F1 score than most latest studies validated on CHB-MIT database. Only the F1 score of the detector developed by [PERSON] et al. [10] is slightly higher than ours. However, due to the long time EEG monitoring for epileptic seizure detection and the scarcity of ictal data, the acquired EEG data in real application scenarios characterize an extremely imbalanced ratio between ictal and interictal segments. Therefore, a high specificity in this application scenario may be equivalent to an unacceptable false alarm rate. For example, a detector with a seemingly high specificity of 95% is expected to trigger six false alarms per hour, which cannot be permitted in a practical application. Compared with the performance of [10], our detector approximately achieves a four fold reduction of FPR (from 2.03/h to 0.44/h) only at the cost of a slight reduction in sensitivity (from 98.27% to 96.69%). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper** & **Year** & **Methods** & **Sensitivity** & **Specificity** & **Precision** & **Accuracy** & **F1** \\ \hline
[4] & 2014 &
\begin{tabular}{c} DWT + Fuzzy Approximate \\ Entropy + SVM Classifier \\ \end{tabular} & 99.3\% & 99.9\% & 99.9\% & 99.6\% & 0.9960 \\ \hline
[46] & 2015 &
\begin{tabular}{c} Discrete Short-Time Fourier \\ Transform (DSTFT) + Multilayer \\ Perceptron (MLP) \\ \end{tabular} & 99.3\% & 97.7\% & 97.74\% & 98.5\% & 0.9850 \\ \hline
[43] & 2016 &
\begin{tabular}{c} Dual-Tree Complex Wavelet \\ Transformation + Normal Inverse \\ Gaussian Parameters \\ \end{tabular} & 100\% & 100\% & 100\% & 100\% & 1 \\ \hline
[42] & 2017 &
\begin{tabular}{c} Wavelet Packet Transform (WPT) \\ Based Entropy + ANN Classifier \\ \end{tabular} & 99.70\% & 100\% & 100\% & 99.85\% & 0.9985 \\ \hline
[41] & 2017 &
\begin{tabular}{c} Tunable-Q Wavelet Transform Based \\ Multi-scale Entropy Features + SVM \\ \end{tabular} & 99\% & 100\% & 100\% & 99.5\% & 0.9950 \\ \hline
[52] & 2017 &
\begin{tabular}{c} Weighted Complex Networks Based \\ Community Structure Detection \\ \end{tabular} & 99\% & 95\% & 95.19\% & 97\% & 0.9706 \\ \hline
[53] & 2017 &
\begin{tabular}{c} Analytic Time-Frequency Flexible \\ Wavelet Transform (ATF- FWT) + \\ Least-Squares Support Vector \\ Machine (LS-SVM) Classifier \\ \end{tabular} & - & - & - & 99\% & - \\ \hline
[54] & 2018 &
\begin{tabular}{c} Subpattern Based PCA and \\ Cross-Subpattern Correlation-Based \\ PCA + SVM Classifier \\ \end{tabular} & - & - & - & 99.5\% & - \\ \hline
[50] & 2018 &
\begin{tabular}{c} A Novel Signal Modeling Approach \\ Based on Discrete Cosine Transform \\ and Hurst Exponent + SVM \\ \end{tabular} & 98\% & 97\% & 97.03\% & 97.50\% & 0.9751 \\ \hline
[55] & 2018 &
\begin{tabular}{c} An Ensemble of Pyramidal \\ One-Dimensional Convolutional \\ Neural Network (P-1D-CNN) \\ Models \\ \end{tabular} & - & - & - & 98.5\% & - \\ \hline
[35] & 2018 &
\begin{tabular}{c} Phase–Amplitude Cross-Frequency \\ Coupling + SVM \\ \end{tabular} & 100\% & 100\% & 100\% & 100\% & 1 \\ \hline
[6] & 2019 & \
\begin{tabular}{c} Hermite Transform + Permutation \\ Entropy, Histogram Features and \\ Statistical Features + SVM Classifier \\ \end{tabular} & - & - & - & 98.5\% & - \\ \hline This Work & - &
\begin{tabular}{c} The Proposed RR-DTDWT (DTDWT \\ + AGRM) + SVM \\ \end{tabular} & 100\% & 100\% & 100\% & 100\% & 1 \\ \hline \hline \end{tabular}
\end{table}