## 3 Results
### Feature-Based Models with Gaussian Noise  
Feature-based models have demonstrated a robust ability to detect anxiety in controlled settings, leveraging well-defined physiological signals and engineered features for effective performance assessment.  
The influence of signal-to-noise ratio on the performance of feature-based models reveals a nuanced impact on their ability to discern stress-related patterns. As _SNR_ decreases, indicating higher noise levels, there was a gradual reduction in model accuracy (Figure 4). Although XGB and DT upheld their superior performance observed when using the original WESAD dataset, the AB model showed a notable decline in efficacy under higher noise conditions, diverging from the other feature-based models. This variation in performance can be expected due to the different ways feature-based models process and interpret data. For instance, models like XGB are designed to handle various types of data irregularities and have mechanisms that can effectively deal with noise to some extent. In contrast, models such as AB might be more sensitive to noise, particularly if the noise disrupts the patterns they rely on for decision-making. Consequently, as the _SNR_ decreases, it is expected that the performance of models would vary, reflecting their individual capacities to filter out noise and maintain accuracy.  
This trend underscores the importance of optimizing noise handling capabilities within feature-based models to ensure their effectiveness in real-world applications where noise variability is common. With noise, performance degraded proportionally with increasing noise (smaller _SNR_ value). These results were consistent for all models, except for AB. Another finding was that XGB outperformed DT and maintained the performance advantage across the range of _SNR_ tested, solidifying its position as the best performing model across conditions tested.  
With respect to feature importance, it was observed that feature importance ranking was also relatively consistent across _SNR_. For example, looking at the feature importance ranking for XGB across different _SNR_ in Figure 5, \(\text{EDA}_{\text{SCL}\_\text{mean}}\) continued to be the top feature across all _SNR_.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**E2E Model** & \
\begin{tabular}{c} **Average Accuracy** \\ **(std)** \\ \end{tabular} &
\begin{tabular}{c} **Average F\({}_{1}\)-Score** \\ **(std)** \\ \end{tabular} & **Accuracy (max)** \\ \hline FCN & 0.79 (0.03) & 0.75 (0.04) & 0.95 \\ ResNet & 0.80 (0.05) & 0.74 (0.07) & 0.96 \\ Time-CNN & 0.76 (0.03) & 0.67 (0.04) & 0.89 \\ MCDCNN & 0.74 (0.03) & 0.65 (0.05) & 0.89 \\ MLP-LSTM & 0.72 (0.02) & 0.60 (0.03) & 0.89 \\ Encoder & 0.69 (0.04) & 0.59 (0.05) & 0.89 \\ MLP & 0.69 (0.01) & 0.59 (0.02) & 0.93 \\ CNN-LSTM & 0.69 (0.02) & 0.54 (0.02) & 0.85 \\ Inception & 0.65 (0.07) & 0.52 (0.07) & 0.91 \\ Random guess & 0.50 & 0.50 & \\ Majority class & 0.53 & 0.23 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: End-to-end model performance results.  
Violin plots (Figure 6) offer a visual exploration of the FB models' performance distribution across the full range _SNR_ (0.001 to 0.6), illustrating the variability and robustness of each model amidst varying noise conditions. These plots combine the features of box plots and density plots, showing not only the median of the F1 scores but also the density plots.  
Figure 4: F1-score performance (mean lines with 5% CI) of feature-based models across various signal-to-noise (SNR) levels.  
Figure 5: Feature importance of the top seven features for the XGB model, using gain, across various signal-to-noise ratios (_SNR_).  
and spread of scores around the median. This visual analysis is particularly insightful as it highlights the more consistent performance of some models, such as KNN, regardless of the noise level, given the narrower distribution. Conversely, models exhibiting wider distributions imply greater variability in performance, which could indicate less reliability in noisy conditions.