## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Physiological Signal** & **Model \& Method** & **Valence** & **Arousal** & **Recognition Interval** \\ \hline BVP, SC & CNN ([PERSON], 2013) [42] & 63.3 & 69.1 & 30 s \\ \hline EEG & SVM ([PERSON], 2014) [43] & 70.9 & 67.1 & 60 s \\ \hline EEG & DBN ([PERSON], 2016) [44] & 66.9 & 69.8 & 30 s \\ \hline EOG, EEG & \begin{tabular}{c} Multimodal deep-learning \\ ([PERSON], 2016) [45] \\ \end{tabular} & 85.2 & 80.5 & 63 s \\ \hline RSP & \
\begin{tabular}{c} Deep sparse auto-encoders \\ ([PERSON], 2017) [39] \\ \end{tabular} & 73.06 & 80.78 & 20 s \\ \hline EEG &
\begin{tabular}{c} Multivariate empirical mode \\ decomposition ([PERSON], 2018) [34] \\ \end{tabular} & 72.87 & 75.00 & 60 s \\ \hline PPG & **Proposed model** & **82.1** & **80.9** & **10 s** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons with other studies using DEAP for classification accuracy and recognition interval. BVP, blood volume pulse; SC, skin conductivity; RSP, respiration. In this study, we used a PPG signal to detect human emotion. The greatest advantage of the use of the PPG signal is that its acquisition procedure is simple. It can be recorded using a small device, such as a cell phone, by wearing it on one's finger. Moreover, the PPG signal changes instantaneously depending on the emotion. Since the recognition interval was 10 s and it could be recorded easily, it could be the most realistic signal for commercialization. We introduced an effective emotion recognition model using a CNN and certain selected statistical features. The main contribution of this paper was the method of extracting features for a PPG signal within a short period of time. As an NN interval of more than 1 min was used for PSD, we used a normalized PPG signal to obtain the PSD for a 10 s signal. In all, we extracted six features for the time domain and 13 features for the frequency domain. Then, we proposed the use of [PERSON]'s correlation to select features related to the valence and the arousal. The result of [PERSON]'s correlation showed that the correlation between the valence and the statistical features was greater than that between the arousal and the statistical features. We selected 10 features with higher correlations for the valence and the arousal. Before that, we did not know whether the features extracted from the 10 s PPG signal or the NN interval were affected by emotions. Another contribution was the feature fusion of deep-learning and statistical features. Both types of features had limitations, so fusing these features could compensate for the disadvantages of each type of feature. The features based on deep-learning did not contain statistical information about the time and the frequency fields, and statistical features did not cover the overall signals. We fused the CNN features related to the normalized PPG signal, CNN features related to the NN intervals, and the selected statistical features. As a result, we achieved a significant performance with a 10 s physiological signal, which had a shorter recognition interval than that considered in a recent study using respiration.