## 4 Discussion  
This study provides an analysis of the resilience of both feature-based and end-to-end models to noise, revealing significant performance disparities. Feature-based models, \begin{table}
\begin{tabular}{c c c c c c} \hline _Architecture_ & _SNR = 0.01_ & _SNR = 0.1_ & _SNR = 0.15_ & _SNR = 0.4_ & _Baseline_ \\ \hline _FCN_ & 0.19 & 0.46 & 0.49 & 0.65 & 0.75 \\ _ResNet_ & 0.14 & 0.36 & 0.41 & 0.70 & 0.74 \\ _Time-CNN_ & 0.01 & 0.09 & 0.09 & 0.27 & 0.67 \\ _MCDCNN_ & 0.01 & 0.07 & 0.10 & 0.17 & 0.65 \\ _MLP-LSTM_ & 0.01 & 0.01 & 0.08 & 0.28 & 0.60 \\ _Encoder_ & 0.04 & 0.08 & 0.08 & 0.20 & 0.59 \\ _MLP_ & 0.01 & 0.01 & 0.06 & 0.17 & 0.59 \\ _CNN-LSTM_ & 0.00 & 0.02 & 0.13 & 0.35 & 0.54 \\ _Inception_ & 0.19 & 0.32 & 0.36 & 0.61 & 0.52 \\ \hline \end{tabular}
\end{table} 
Table 6: F1-score performance results of end-to-end models with different levels of noise. Figure 8: F1-score of top 5% performing iterations of end-to-end models (midline with 95–100% range) across various noise levels. particularly XGB, demonstrated superior robustness across all noise conditions, maintaining higher accuracy levels compared to end-to-end models. This finding supports our initial hypothesis that some models would exhibit enhanced resistance to noise, highlighting the effectiveness of feature-based models in noisy real-world scenarios. In comparison to state-of-the-art results, where the average accuracy for detection of anxiety has been found to be 0.82 (95% CI = 0.71-0.89) [56], feature-based models such as DT and XGB, with a 0.99 accuracy as seen in Table 3, outperformed this range of performance in baseline measures. In addition, end-to-end models such as FCN, ResNet, Time-CNN, MCDCNN, and MLP-LSTM, with an average baseline performance from 0.72-0.79 accuracy as seen in Table 5, were comparable with current state-of-the-art [56]. All feature-based models performed consistently across different SNR samples, yielding a uniform distribution across the samples, except for AB. Even though KNN was not the highest performer, it maintained a consistent performance level, which may have been due to potential underfitting, as further optimization of KNN has been shown to improve performance [57]. Finally, even though XGB and DT performed closely, the performance distribution for XGB was tighter than that of DT, solidifying it as the best choice under the given conditions. EDA appeared to be the most important modality, with \(\text{EDA}_{\text{scl max}}\) being the most important feature. Even though EDA was the most important modality, it may be of concern, since skin conductance can easily be disrupted in the presence of physical activity or sweat-inducing warm weather. BVP can be used as a supplementary signal to support model detection in the case where EDA quality is reduced. For end-to-end models, while it may be surprising to see that non-recurrent models outperformed recurrent models, the relatively short-time frame associated with anxiety may be captured well enough with the convolution layer of the CNN models, such that it was able to preserve some contextual information from the recent past, providing an equivalence to the memory cell in RNN networks. Conversely, end-to-end models exhibited a significant reduction in performance with the introduction of noise, with FCN outperforming other models under these conditions yet still falling short of the robustness displayed by feature-based models. ResNet, while initially performing well, saw a decline in effectiveness at lower SNR levels, underscoring the limitations of end-to-end models in handling noisy data. The consistency of dominant features in maintaining their predictive power, even in noisy environments, was evident across both model types. This consistency challenges the expectation that end-to-end models would inherently handle noise better due to their capacity to learn complex patterns directly from raw data.