### Hyperparameter Tuning/Analysis
(2019) (2019), uses a ResNet-based architecture on 510 participants but performs poorly, with MAEs well exceeding the AAMI standard threshold of 5 mmHg for both systolic blood pressure and diastolic blood pressure. Not only do the results fail to reach the AAMI requirements, but the model apparently requires lengthy training periods (up to 10,000 epochs), indicating problems with training efficiency and scalability. [PERSON] and [PERSON] (2021) (2021) apply a multi-type feature fusion (MTFF-ANN) technique to a small dataset of 11,546 samples only, yielding a better but still insufficient systolic MAE. While the diastolic estimate meets the AAMI \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline cBP-Tnet & \multirow{2}{*}{\(d_{\text{model}}\)} & \multirow{2}{*}{\(h\)} & \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(P_{\text{drop}}\)} & \multirow{2}{*}{\begin{tabular}{c} **Grad** \\ **Clip** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **SBP MAE** \\ **(mmHg)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **DBP MAE** \\ **(mmHg)** \\ \end{tabular} } \\
**Tuning/Analysis** & & & & & & & & \\ \hline cBP-Tnet (_Base_) Model & **128** & **4** & **8** & **0.05** & **4.0** & **4.71** & **2.34** \\ \hline \multirow{3}{*}{**(A)**} & 64 & & & & & 7.25 & 3.72 \\  & 256 & & & & & 5.03 & 2.55 \\ \hline \multirow{3}{*}{**(B)**} & 2 & & & & & 6.02 & 3.15 \\  & 8 & & & & 4.75 & 2.36 \\ \hline \multirow{3}{*}{**(C)**} & & & 6 & & 4.77 & 2.42 \\  & & & 10 & & 4.76 & 2.37 \\ \hline \multirow{3}{*}{**(D)**} & & & & 0.00 & & 4.74 & 2.37 \\  & & & & 0.10 & & 4.87 & 2.46 \\ \hline \multirow{3}{*}{**(E)**} & & & & & 0.0 & 145.43 & 69.72 \\  & & & & & 8.0 & 4.75 & 2.36 \\ \hline \hline \multicolumn{3}{l}{_cBP-Tnet (Extended)_} & \multirow{3}{*}{_128_} & \multirow{3}{*}{**4**} & \multirow{3}{*}{**8**} & \multirow{3}{*}{**0.05**} & \multirow{3}{*}{**4.0**} & \multirow{3}{*}{**4.32**} & \multirow{3}{*}{**2.18**} \\ \multicolumn{3}{l}{_Model_} & & & & & & \\ \hline \hline \end{tabular} _Note:_ **Bold** font indicates the base model configuration and its resulting MAE; _Bold-Italic_ fonts denotes the extended model configuration. Experiments (A)–(E) refer to the per-parameter sweeps described in the text.  
\end{table} 
Table 3: _cBP-Tnet_ AI model hyperparameter tuning/analysis. criteria, the very complex and difficult automatic feature extraction process and the small sample size call into question the model's applicability to larger populations and situations. [PERSON] et al. (2024) (2024) incorporate temporal convolutional networks and a CBAM attention mechanism over a huge dataset of 270,488 samples to achieve even greater improvements. Although the diastolic MAE is high and meets the AAMI standard, systolic estimation fails, and the model takes at least 1500 epochs to converge. This intricacy and extensive training burden indicate a smart but not totally efficient system. In contrast, the proposed _cBP-Tnet_ model that makes use of a multi-task learning Transformer-based network outperforms all the recent studies. It was based on 500 random patients' records (202,956 samples); it achieves significantly lower MAEs for both systolic blood pressure (4.32 mmHg) and diastolic blood pressure (2.18 mmHg), meeting the AAMI requirements. Remarkably, this strategy reduces the training time to only 1295 epochs, a 13.67% improvement over its latest competitor ([PERSON] et al., 2020), while employing a simpler single-model deep learning framework. This not only simplifies model design but may also reduce computational and infrastructure demands. The proposed _cBP-Tnet_ AI model demonstrates that systematic improvements in model architectures, sample sizes, and training protocols can finally close the clinical standard gap, paving the way for more practical and widespread use of continuous non-invasive blood pressure monitoring tools, optimizing the potential to be integrated/enable non-invasive continuous monitoring by home and mobile healthcare devices.