# A Photoplethysmogram Dataset for Emotional Analysis
## 3 Results and Discussion
### Comparison to Conventional PPG Dataset  
Table 13 lists datasets for emotional analysis which include PPG signals. The most popular way to invoke participants' emotions has been utilizing video clips, because video clips easily make people immerse in the scenes. DEAP [30], WESAD [13], and CLAS [14] utilized music videos, while CASE [15] utilized movie clips. The main difference between music videos and movie clips as the source of invoking emotions is the importance of the story. Music videos often emphasize music and short but sensational scenes, while the images in the movies are arranged to tell stories in general. However, it is difficult to tell a story in a highlighted movie scene of a few minutes because the original movie lasts more than an hour.  
The proposed dataset utilized narrative video clips to invoke participants' emotions except relaxed, as it was described in Section 2.1. The stories in the video clips were shown as they were created by the original producers because they were shorter than 5 min, and the impact of the short clips was verified by the number of viewers. The influence of the stories in the video clips would be an interesting topic for further studies using the proposed dataset.  
The biggest limitation of the proposed dataset would be the limited number of participants, which makes it difficult to train a big network model and to verify it. This limitation could be overcome when the proposed dataset is utilized together with other datasets. It would be interesting for further studies to apply transfer learning for the proposed dataset after training a network with a big dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{4}{c}{**Accuracies (\%) for Each Iteration**} & \multirow{2}{*}{**Mean \(\pm\) Std.**} \\ \cline{2-2} \cline{4-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline B1 & 66.38 & 54.78 & 59.13 & 85.22 & 68.7 & 66.84 \(\pm\) 10.45 \\ B2 & 83.62 & 91.3 & 65.22 & 64.35 & 85.22 & 77.94 \(\pm\) 11.05 \\ B3 & 72.41 & 73.91 & 74.78 & 88.7 & 70.43 & 76.05 \(\pm\) 6.5 \\ B4 & 58.62 & 73.91 & 64.35 & 89.57 & 71.3 & 71.55 \(\pm\) 10.48 \\ B5 & 87.93 & 82.61 & 76.52 & 77.39 & 59.13 & 76.72 \(\pm\) 9.7 \\ B6 & 84.48 & 69.57 & 80 & 50.43 & 64.35 & 69.77 \(\pm\) 12.04 \\ B7 & 67.24 & 85.22 & 59.13 & 53.91 & 62.61 & 65.62 \(\pm\) 10.72 \\ B8 & 85.34 & 57.39 & 68.7 & 60.87 & 85.22 & 71.5 \(\pm\) 11.83 \\ B9 & 71.55 & 64.35 & 66.09 & 73.91 & 71.3 & 69.44 \(\pm\) 3.61 \\ B10 & 58.62 & 73.04 & 67.83 & 80.87 & 78.26 & 71.72 \(\pm\) 7.94 \\ B11 & 56.9 & 84.35 & 78.26 & 71.3 & 80 & 74.16 \(\pm\) 9.6 \\ B12 & 87.93 & 75.65 & 80.87 & 57.39 & 82.61 & 76.89 \(\pm\) 10.51 \\ B13 & 69.83 & 79.13 & 79.13 & 71.3 & 53.91 & 70.66 \(\pm\) 9.22 \\ B14 & 80.17 & 79.13 & 77.39 & 64.35 & 85.22 & 77.25 \(\pm\) 6.96 \\ B15 & 81.9 & 80 & 73.91 & 87.83 & 66.96 & 78.12 \(\pm\) 7.13 \\ B16 & 79.31 & 57.39 & 69.57 & 52.17 & 78.26 & 67.34 \(\pm\) 10.92 \\ B17 & 63.79 & 84.35 & 56.52 & 73.91 & 84.35 & 72.58 \(\pm\) 11.08 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Accuracies of different network structure of the proposed dataset for valence classification. The training and test were repeated five times.