### cBP-Tnet Evaluation and Visualization  
Figures 3 and 4 show how well a _cBP-Tnet_ AI model predicts systolic and diastolic blood pressure values from specific input signals. In Figure 3, the training loss (blue line) begins at a very high value and rapidly drops in the initial epochs, demonstrating that the model is effectively learning the underlying patterns in the data. As training develops beyond the early stages, the loss gradually decreases, eventually stabilizing at a lower value. The validation loss (yellow line), while initially higher, follows a general downward trend, indicating that the model is generalizing to previously unseen data rather than overfitting. By 1295 epochs, the training and validation losses appeared to have stabilized, indicating that the chosen training duration was appropriate as it was designed to save the best model based on the best combined validation L1 loss. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Related Deep Learning Methods** & **SBP (MAE, mmHg)** & **DBP (MAE, mmHg)** \\ \hline ResNet & 9.43 (\(r=\) N/A) & 6.88 (\(r=\) N/A) \\ MTFF-ANN & 5.59 (\(r=\) 0.92) & 3.36 (\(r=\) 0.86) \\ TCN-CBAM & 5.35 (\(r=\) 0.80) & 2.12 (\(r=\) 0.60) \\ _cBP-Tnet_ & **4.32 (\(r=\) 0.89)** & **2.18 (\(r=\) 0.87)** \\ \hline \hline \end{tabular} _Note: **Bolidface and italics** denote the proposed _cBP-Tnet_ method.  
\end{table} 
Table 4: Deep learning-based methods related to estimate blood pressure with automatic photo-plethysmogram feature extraction mechanisms. The model's performance was better highlighted in the SBP and DBP prediction against the actual plots, as shown in Figure 4. The predicted SBP (dashed red line) closely reflects the real SBP (solid blue line) values across the sample series, suggesting a high capacity to simulate the dynamic changes of systolic pressure. Similarly, while DBP fluctuates to a greater extent and considering that the peaks and troughs may not coincide completely, the predicted values (green line) show a general relationship with the actual data (yellow line). This shows that the model has learned the most common patterns in DBP; however, it may be more susceptible to noise or more complex fluctuations in diastolic pressure. The predicted vs. actual scatter graph test results for systolic blood pressure (SBP) in Figure 5 and diastolic blood pressure (DBP) in Figure 6 show how the model's anticipated values compare to the actual measurements. Each point represents a single test case, with the horizontal axis representing the actual measured blood pressure and the vertical axis representing the model's predicted value for that same instance. These plots demonstrates the performance of the model within different blood pressure ranges. A strong linear trend, as seen in both graphs, implies that the model is capturing a significant portion of the underlying pattern: correlation coefficients of approximately 0.885 for SBP and 0.874 for DBP indicate a high degree of linear relationship. The red regression lines fitted across the data points, with slopes of approximately 0.87 for SBP and 0.84 for DBP  
Figure 4: _cBP-Tnet_ AI model SBP (**top**) and DBP (**bottom**) prediction vs. actual (test) plots. Figure 3: _cBP-Tnet_ AI model training and validation loss plot for SBP and DBP—optimal epochs: 1295.  
and moderate intercept values, demonstrate that forecasts often grow in tandem with actual values. However, the appearance of substantial scatter around these regression lines indicates that, while the model's predictions are frequently accurate, they are not flawless. Some examples fall well above or below the line, indicating over- or under-prediction. Nevertheless, these plots provide a visual assessment of prediction accuracy, demonstrating that the model performs well for a substantial portion of the data but has room for improvement in terms of precision and consistency.