## 4 Discussion of Obtained Results  
Obtained results are structured by the applied stress evaluation methods: at first, using the GMM-based classification parameters \(SP_{\text{GMM}}\) from the phonation signals, next the statistical parameters \(SP_{\text{PPG}}\) determined from the PPG signals (both for \(MF_{\text{1-4}}\) measuring phases), and finally the stress evaluation rates for \(MF_{\text{2}}\) to \(MF_{\text{4}}\) phases are calculated by the fusion of the \(SP_{\text{GMM}}\) and \(SP_{\text{PPG}}\) parameters. Summary results are next divided by gender of a tested person--values for groups of males, females, and for all participating persons are subsequently visualized and compared. Within the GMM classification part, an auxiliary analysis was also performed to evaluate an influence of the database used for GMMs creation and training. Comparison of \(L_{\text{STRESS}}\), \(L_{\text{NORMAL}}\) and \(\Delta L_{\text{S-N}}\) values in Table 5 shows that all three tested databases are usable for this purpose. As shown in the last column, the greatest differences between \(L_{\text{STRESS}}\) and \(L_{\text{NORMAL}}\) values are obtained when the Emo-DB speech database was used. Therefore, in further analysis, the GMMs were created and trained with the help of the database DB\({}_{\text{2}}\). Next, we analyzed the percentage distribution values of the output classes \(C_{\text{1N}}\), \(C_{\text{2S}}\), and \(C_{\text{3O}}\) per each vowel of the phonation signal. The representative results from this analysis performed on the recorded vowels are shown in detail in Figure 9, where a non-uniform class distribution can be seen for vowels recorded in the measuring phases \(MF_{\text{1-4}}\). However, the summary comparison in Figure 10 demonstrates the expected trends of \(L_{\text{STRESS}}\) and \(L_{\text{NORMAL}}\) values being in correlation with mean \(RO_{\text{C1N},\text{C2S},\text{C3O}}\) values calculated for all five vowels together--\(RO_{\text{C2S}}\) values are increased in \(MF_{\text{2,3}}\) phases in comparison to \(MF_{\text{1,4}}\) phases. This trend is accompanied with parallel decrease of \(RO_{\text{C1N}}\) values in \(MF_{\text{2,3}}\) phases and increase in \(MF_{\text{1,4}}\) phases. \begin{table}
\begin{tabular}{c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\  
The results obtained by the second evaluation approach confirm our assumption that the stress level evoked by scanning in the tested MRI device is identifiable and measurable using HR values determined from the PPG signal. From the detailed analysis of filtered HR values concatenated for the recording phases PPG11-42 together with their LT parameter follows that, in the measuring phases \(MF_{2}\) and \(MF_{23}\), there is a pronounced increase in the mean HR with a positive LT, while the last phase \(MF_{4}\) has typically lower mean HR and negative LT. This increase of mean HR values is accompanied by higher variation of discrete HR values. In the first measuring phase \(MF_{1}\), lower HR with positive LT is observed. In addition, there are visible differences in HR values determined from the recording phases PPG11 and PPG12. This was probably due to the load effect of speech (vowels) production by a tested person manifested by a small increase of the mean HR determined from PPG signals recorded after phonation.