## 4 Experimental Results
### Experimental Results  
As an emotion classification task is a binary classification, the classification accuracies of valence and arousal are shown in Table 4. This table presents the accuracies of valence and arousal according to the model architectures. The fully connected model that used all three inputs was trained with fully connected layers rather than using the CNN architecture. The CNN-only model used the first CNNarchitecture, which did not add any statistical features and the CNN with the NN intervals. The CNN with the selected statistical features model represented the neural network with the first CNN and the selected statistical features and did not add the CNN with the NN intervals. The two-CNN models with the statistical features was trained with the first CNN, the second CNN, and all 19 statistical features. Lastly, the proposed model, which was composed of the first CNN, the second CNN, and the selected statistical features, showed the best performance for both valence and arousal in this experiment. The accuracy for valence was 82.1%, and the accuracy for arousal was 80.9%. Overall, the results indicated that it was possible to recognize emotions from only a 10 s PPG signal. It was essential to use the CNN architecture since the fully connected model could not recognize emotion well. Moreover, the models adding statistical features represented a significant performance improvement: on average, the classification accuracy increased by 3%. In particular, the statistical features for valence had a more considerable effect on accuracy than the features for arousal. This was inferred from the results that the Pearson's correlations for valence were higher than those for arousal. Furthermore, we could achieve better performance by using the NN intervals. Comparing the use of all the statistical features with that of the selected statistical features, we found that the selected features improved the performance. This indicated that the features that were not related to emotions were not suitable for the classification of emotion. As a result, using not only deep-learning features, but also statistical features showed better results. Table 5 represents the comparisons with other studies using the DEAP dataset, which covered the classification accuracy and the recognition interval. Various deep-learning methods and physiological signals were used to obtain the better performance. Furthermore, more complicated deep-learning models were introduced, such as DBN, multimodal deep-learning, and auto-encoder. In general, the accuracy increased, and the recognition interval shortened. As shown in this table, we could prove that the proposed model outperformed the other methods in terms of the classification accuracy and the recognition interval. In particular, our method could detect emotion for a shorter interval with a better performance. Even the recent research using an auto-encoder with an RSP signal, focused on fast emotion recognition, had a 20 s recognition interval, which was double the recognition interval of the proposed method. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model Architecture** & **Valence** & **Arousal** \\ \hline Fully-connected model & 56.1 & 55.0 \\ \hline CNN only & 75.8 & 76.3 \\ \hline CNN with selected statistical features (First CNN) & 79.8 & 78.9 \\ \hline Two CNNs with statistical features & 81.2 & 79.9 \\ \hline Two CNNs with selected statistical features & **82.1** & **80.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental result according to the model architecture.