## 3 Results
### Normalization Used for Napcc and Optimum Number of Napcc
These results clearly indicate that the performance of the proposed method was superior to that of our previous method even in noisy environments. Figure 5a,b shows the F1 scores of the subjects obtained via the proposed method or our previous method in Exp-1 and Exp-2, respectively. Please note that herein, the mean F1 score value was used after leave-one-out cross-validation was conducted 10 times. It can be observed from these figures that the F1 score of the proposed method in each subject exceeded the F1 score of the conventional method in most cases. In particular, the results of Exp-2 suggest that the use of the proposed method improved the detection performance of subjects for whom detection was difficult with the conventional method. Exp-1 demonstrates that in most cases, the proposed method worked better than the previous method. In particular, for the recorded data (No. 9 and 10) of subjects who were not detected, the F1 score of the proposed method was improved by about 20-30% compared with that of the previous method. These findings suggest that the proposed method was more effective than the conventional method even when SBEs were detected from the recorded data which contained low-SNR non-SBEs and SBEs. Regarding subject No. 10 in Exp-1 and subject No. 5 in Exp-2, a degradation in performance was found compared to the results obtained from the other subjects. This could be due to the shorter duration of low-intensity SBEs. It was considered that in cases of practical use of this proposed method, the recorded data used in Exp-1 and in Exp-2 would be mixed together; therefore, in subsequent experiments, using two classifiers, namely, MLP-ANN and RBF-ANN, the effectiveness of the proposed method was validated for recording datasets constructed by mixing together the recorded data used in this study. Table 6 lists the performance outcomes of the proposed method for the mixture of the data used in Exp-1 and Exp-2 using two classifiers, i.e., MLP-ANN and RBF-ANN. These results suggest that, irrespective of the type of ANN, the proposed method can detect SBEs with higher accuracy than the conventional method, even for mixed recorded data. \begin{table}
\begin{tabular}{l c c c c c} \hline  & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** & **PPV [\%]** & **NPV [\%]** & **F1 Score [\%]** \\ \hline Proposed method & 85.83 \(\pm\) 7.90 & 81.75 \(\pm\) 11.98 & 91.95 \(\pm\) 7.72 & 83.81 \(\pm\) 18.23 & 85.45 \(\pm\) 14.99 & 80.48 \(\pm\) 12.25 \\ Previous method & 82.59 \(\pm\) 10.14 & 82.84 \(\pm\) 12.91 & 87.86 \(\pm\) 13.07 & 79.56 \(\pm\) 22.29 & 85.45 \(\pm\) 15.70 & 77.57 \(\pm\) 14.48 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation results of the proposed and conventional methods in Exp-1. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** & **PPV [\%]** & **NPV [\%]** & **F1 Score [\%]** \\ \hline Proposed method & 85.99 \(\pm\) 5.69 & 79.64 \(\pm\) 9.50 & 91.34 \(\pm\) 7.18 & 82.87 \(\pm\) 15.80 & 87.31 \(\pm\) 8.63 & 79.81 \(\pm\) 9.14 \\ Previous method & 75.64 \(\pm\) 18.80 & 73.64 \(\pm\) 17.34 & 81.97 \(\pm\) 26.06 & 75.65 \(\pm\) 15.80 & 83.81 \(\pm\) 11.42 & 69.40 \(\pm\) 15.85 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation results of the proposed and conventional methods in Exp-2.