# A Photoplethysmogram Dataset for Emotional Analysis
## 3 Results and Discussion
### Emotional Classification Results  
The data were divided into three groups: training, validation, and testing, at a respective ratio of 7:1:2. We used the Adam optimizer, and the learning rate was set to 0.001. Binary cross-entropy was chosen for the loss function because the output node of the presented network was binary. The network was trained for 200 epochs with a batch size of eight. K-fold validation was used to obtain reliable results, and the number of K-folds is five. Tables 9 and 10 list the accuracies of previous studies and the proposed dataset for arousal classification. The classification accuracies with the proposed dataset varied according to the network structure. The best accuracy among the different network structures was 75.16% with an LSTM layer, which was similar to the accuracy in [29]. We found that the accuracy varies according to the selection of the training dataset. The best accuracy among the five trials was 81.9%, while the lowest was 64.35%. The total average accuracy for all the network structures was 66.94%, and any RNN blocks increased the accuracy when they were combined with the base network structure (B1). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Participant IDs**} & \multicolumn{3}{c}{**Sadness**} & \multicolumn{3}{c}{**Relaxed**} & \multicolumn{3}{c}{**Joy**} & \multicolumn{3}{c}{**Anger**} \\ \cline{2-13}  & **D*** & **A** ** & **V** ** & **D*** & **A** ** & **V** ** & **D*** & **A** ** & **V** ** & **D*** & **A** ** & **V** ** \\ \hline  
\hline Avg. & 7.22 & 3.83 & 3.00 & 6.61 & 2.58 & 6.33 & 7.00 & 6.67 & 7.17 & 7.61 & 7.42 & 3.00 \\ Std. & 2.13 & 1.90 & 1.60 & 1.91 & 1.00 & 1.61 & 1.75 & 1.72 & 1.27 & 1.79 & 1.88 & 0.74 \\ \hline \hline \end{tabular}  
* D denotes the degree of each emotional status (sadness, relaxed, joy, and anger). ** A and V denote arousal and valence, respectively.  
\end{table} 
Table 8: Participants’ responses on the degree of emotional stimuli. Likewise, Tables 11 and 12 list the accuracies of previous studies and the proposed dataset for valence classification. The average accuracy for all the variable blocks was 72.60% which was higher than the arousal, and the best mean accuracy was 78.12% with GRU, which was 2.96% points higher than the arousal. This was higher than the accuracies in previous studies, which means the valence were invoked well with the video clips in our experiments. The accuracies with GRU layers were higher than with LSTM layers in general in our experiment. The recognition accuracies of emotional states using PPG solely were relatively lower than the best accuracies of using other signals such as ECG and EEG. [PERSON] and [PERSON] achieved 96.7% and 97.3% accuracies for binary classifications of arousal and valence, respectively [36]; [PERSON] et al. reported 98.06% accuracy by utilizing ECG together with EEG [37]. However, there are possibilities for the accuracies with PPG to be increased in further studies. A report presented that the emotion recognition using PPG and ECG showed similar accuracy [14], and studies utilizing PPG solely have not been much studied yet. Although the accuracies were low (71.6% for arousal, 71.7% for valence), PPG together with GSR achieved slightly better accuracy than the combination of ECG and GSR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** & **Database** & **Signal Source** & **Method** & **Accuracy (\%)** \\ \hline
[32] & 2021 & DEAP & PPG & CNNs with statistical features & 72.40 \\
[29] & 2020 & DEAP & PPG & CNNs with statistical features & 75.3 \\
[22] & 2021 & DEAP & PPG & Adaboost & 64.84 \\
[14] & 2019 & CLAS & PPG + GSR & SVM & 71.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Valence classification accuracies in previous studies.