## 3 Results
### Classification of Perceived Pain Intensity Levels
#### 3.6.1 Classification Between Intensity Levels 6 and 7  
We began the analysis by calculating the PFI to identify the most influential features for classifying intensity levels 6 and 7 (Figure 4). This was performed on two datasets: PHY_12 and PHY_12 + VAL_ARO. The results indicate that PTT, SCR, and pNN50 were the top three features in the PHY_12 dataset, whereas valence, PTT, and SCR ranked highest in the PHY_12 + VAL_ARO dataset. Table 8 and Figure 5 summarize the classification performance for distinguishing intensity levels 6 and 7 across the three feature sets based on PFI analyses. The first set (VAL_ARO) included only valence and arousal; the second (PHY_Sel) featured the top three from PHY_12; and the third (Combined_Sel) incorporated the top three from PHY_12 + VAL_ARO. Combined_Sel (valence, PTT, and SCR) achieved the highest test performance, with an average F1 score of 0.733, surpassing PHY_Sel (0.696) and VAL_ARO (0.611). A similar trend was observed for balanced accuracy, where Combined_Sel had the highest balanced accuracy of 0.737. Regarding class-specific metrics, Combined_Sel provided superior recall and precision for intensity levels 6 and 7, indicating better overall performance. The training F1 scores were slightly higher than the test F1 scores, with \begin{table}
\begin{tabular}{c c c c c} \hline
**F1 (Test)** & **Accuracy** & **Recall** & **Specificity** & **F1 (Training)** \\ \hline
0.834 \(\pm\) 0.010 & 0.830 \(\pm\) 0.011 & 0.855 \(\pm\) 0.014 & 0.805 \(\pm\) 0.020 & 0.867 \(\pm\) 0.003 \\ \hline \end{tabular}
\end{table} 
Table 7: Performance measures for classifying baseline and emotional states using top three ranked physiological features: SCR, SCL, and RESP. differences ranging from 0.053 to 0.075. Normalized confusion matrices (Figure S4) show that VAL_ARO correctly classified 55% of intensity 6 and 70% of intensity 7, whereas PHY_Sel improved these rates to 66% and 74%, respectively. Combined_Sel achieved 70% and 77%, aligning with its highest F1 score. Despite using only the three highest-ranked physiological features, the classification accuracy was approximately 0.70, in contrast to the valence and arousal measures, which  
Figure 4: Permutation importance analysis for classifying intensity levels 6 and 7. (**Left**) Analysis using 12 physiological features. (**Right**) Analysis using physiological features combined with valence and arousal. The pink bars indicate arousal and valence. Figure 5: Performance measures for classifying intensity levels 6 and 7: (**left**) F1 score and balanced accuracy; (**right**) recall and precision for each intensity. VAL_ARO represents valence and arousal, PHY_Sel denotes the top three ranked physiological features (PTT, SCR, and pNN50), and Combined_Sel refers to the top three ranked features from combined physiological and valence–arousal features (valence, PTT, and SCR). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Data** & **F1 (Test)** &
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & **Recall (6)** & **Precision (6)** & **Recall (7)** & **Precision (7)** & **F1 (Training)** \\ \hline VAL_ARO & 0.611 \(\pm\) 0.030 & 0.628 \(\pm\) 0.028 & 0.554 \(\pm\) 0.068 & 0.691 \(\pm\) 0.052 & 0.701 \(\pm\) 0.062 & 0.625 \(\pm\) 0.041 & 0.666 \(\pm\) 0.005 \\ \hline PHY\_Sel & 0.696 \(\pm\) 0.029 & 0.700 \(\pm\) 0.027 & 0.663 \(\pm\) 0.055 & 0.723 \(\pm\) 0.022 & 0.738 \(\pm\) 0.026 & 0.697 \(\pm\) 0.036 & 0.771 \(\pm\) 0.012 \\ \hline Combined\_Sel & 0.733 \(\pm\) 0.013 & 0.737 \(\pm\) 0.014 & 0.705 \(\pm\) 0.035 & 0.758 \(\pm\) 0.022 & 0.769 \(\pm\) 0.033 & 0.737 \(\pm\) 0.023 & 0.786 \(\pm\) 0.006 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance measures for classifying intensity levels 6 and 7.  
achieved an accuracy of approximately 0.61. These findings highlight the significant role of physiological measures in differentiating between intensity levels 6 and 7.