## 7 The Estimation Models Discussion  
In Table 4, the regression RMSE values are presented. These values indicate how well the linear regression models fit our datasets. Specifically, subject 1's model displays the lowest RMSE values, with 4.4 mmHg for SBP and 1.5 mmHg for DBP, indicating a stronger linear correlation among their features. In contrast, subject 2's models display the highest RMSE values among the 3 datasets, measuring 10.60 mmHg for SBP and 5.9 mmHg for DBP, showing a weaker linear correlation among their features compared to subject 1's model. As a result, the models that combine information from both subjects fall in between the RMSE values of 7.5 mmHg for SBP and 3.83 mmHg for DBP.  
Furthermore, it is worth highlighting that we observed smaller RMSE and STD values in the models designed for estimating DBP in contrast to those for SBP. This discrepancy may be attributed to the lower variance in DBP. A similar trend is observable when comparing the RMSE and STD of the fits between subject 1's subset and subject 2's, where the latter shows larger values due to lower data quality and more varied signal (Figure 1).  
The leave-out-cross validation was used to assess the model's capacity for generalization to an independent dataset, as displayed in Table 5. It is important to note that models derived from the two subjects are limited by the number of subjects used in the data, making it unlikely for the models to accurately estimate the SBP and DBP of subjects other than subjects 1 and 2. Still, our model shows lower MAE and STD values when compared with the parameter-based CNN and Bidirectional Long Term Memory model performed in [31] with MAE (7.849 mmHg), RMAE (11.503 mmHg), MAE (4.418 mmHg), and RME (6.525 mmHg) for the SBP and DBP, but without any feature extraction. Moreover, we also compared our results with studies using machine generalized regression neural networks (GRNNs) developed on the MIMIC II database in [41], K-nearest neighbors' algorithm (KNN) by [PERSON] et al. [42], and the machine learning method implemented by [PERSON] et al. in [43], shown in Table 5.  
The models that exclusively utilize information from subject 1 show lower MAE, STD, and RMSE values when compared with those based on subject 2. This difference shows the former models' enhanced ability to estimate subject 1's SBP and DBP compared to the latter models' performance in predicting subject 2's values. The variability in subject 2's signals is a probable factor influencing the accuracy of the models' predictions. The performance of the models using data from one subject when applied to the other subject's data was also  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training Set**} & \multicolumn{2}{c}{**Systolic BP**} & \multicolumn{2}{c}{**Diastolic BP**} \\ \cline{2-5}  & **RMSE** & **Variance** & **RMSE** & **Variance** \\  & **(mmHg)** & **(mmHg2)** & **(mmHg)** & **(mmHg2)** \\ \hline Subject 1 and 2 & 7.5 & 78 & 3.83 & 52.1 \\ \hline Subject 1 only & 4.40 & 38.49 & 1.5 & 6.6 \\ \hline Subject 2 only & 10.60 & 170.2 & 5.9 & 51.49 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated RMSE and RMSE of the linear models for each training subset with corresponding SBP and DBP variance.  
assessed, as shown in Table 5. Notably, the MAE, STD, and RMSE values are greater in this scenario in comparison to the error values reported in Tables 4 and 5. These models are specifically tailored to each subject due to the training set relying on one of the subjects. Therefore, the accuracy decreases when predicting the SBP and DBP of the other subject.