### Hyperparameter Tuning/Analysis  
Table 3 summarizes hyperparameter tuning experiments performed on the proposed _cBP-Tnet_ model to increase its capabilities to estimate systolic blood pressure and diastolic blood pressure (SBP and DBP) from raw PPG input data. Each row represents a change in one or more model parameters, including the model dimensionality (\(d_{\text{model}}\)), the number of heads (\(h\)) in the multi-head attention mechanism, the number of encoder blocks (\(N\)), the probability of dropout (\(P_{\text{drop}}\)), and the gradient clipping threshold (grad_clip). Along with these parameter settings, the table shows the corresponding mean absolute error (MAE) values for SBP and DBP, which indicate how well the model predicts blood pressure relative to genuine observed results--the lower the MAE, the better. In the \"Base\" configuration, where \(d_{\text{model}}=128\), \(h=4\), \(N=8\), \(P_{\text{drop}}=0.05\), and grad_clip \(=4.0\), the model originally trained for 150 epochs (10% of the previous state-of-the-art one [5]) produces MAEs of 4.68 mmHg for SBP and 2.36 mmHg for DBP. The next rows (A through E) indicate proposed systematic experimental trials that could be carried out to change important parameters. In row (A), the model dimensionality is either reduced to 64 or increased to 256. We observe that a smaller dimensionality significantly increases error (SBP MAE \(\sim 7.25\) mmHg), while a larger dimensionality (256) improves performance slightly (SBP MAE \(\sim 5.03\) mmHg) but not beyond the original baseline. Similarly, in row (B), decreasing the number of attention heads from four to two impairs forecasts, whereas increasing to eight heads brings SBP MAE closer to the original values but with no substantial improvement over the baseline. Part C adjusts the number of encoder layers (N), comparing 6 and 10 layers to the original 8. Adjustments to the depth result in minor error variations, but no significant improvement in performance (SBP MAE \(\sim 4.76\)-4.77 mmHg and DBP MAE \(\sim 2.37\)-2.42 mmHg). Part (D) tests various dropout probabilities. A 0% dropout rate results in comparable performance (SBP MAE \(\sim 4.74\) mmHg) to the baseline, indicating that a  
moderate dropout value may provide stability but is not necessary. A greater dropout rate (0.10) somewhat decreases performance, implying that excessive dropout may degrade model accuracy. Finally, part (E) examines the impact of gradient clipping. Eliminating gradient clipping (0.0) results in a catastrophic failure (SBP MAE rises to 145.43 mmHg), indicating that uncontrolled gradients significantly disrupt training. In contrast, a high gradient clip value (grad_clip = 8.0) returns performance to the baseline, demonstrating the necessity of managing gradient explosions. By the end of these extensive experimental trials and hyperparameter tuning/analysis, the researchers identified the final optimal configuration: \(d_{\text{model}}=128,\ h=4,\ N=8,\ P_{\text{drop}}=0.05,\) and grad_clip = 4.0. Training the model for up to the optimal 1295 epochs (based on the best combined SBP/DBP validation \(L_{1}\) loss) yielded a noticeable improvement in accuracy, reducing the SBP MAE to 4.31 mmHg and the DBP MAE to 2.18 mmHg. This showed that careful and methodical tuning of network architecture and training parameters is necessary to achieve robust performance in blood pressure prediction tasks, and that certain settings--particularly controlling model size, maintaining moderate dropout, and preventing gradient explosion--are key to achieving high-quality, stable results. Comparison Against Related Deep Learning Methods to Estimate Blood Pressure with Automatic Feature Extraction Using Photolethysmogram Feature Extraction  
Table 4 summarizes the progress of research methods and performance benchmarks for non-invasive continuous blood pressure estimation using PPG signals with automatic feature extraction mechanisms. The top row, representing [PERSON] et al.