## 6 Conclusions  
In this paper, we introduce a cuff-less, beat-by-beat blood pressure estimation model, which combines temporal convolutional networks (TCNs) with attention mechanisms. We have modified the standard TCN framework by integrating a CBAM, which allows the model to better identify and focus on critical features, thereby enhancing the precision of blood pressure estimations. We validated our model on a large-scale, publicly available dataset of physiological signals. Specifically, on the UCI physiological signal dataset, our model achieved an absolute mean error and standard deviation of 5.3482 mmHg and 8.3410 mmHg, respectively, for systolic blood pressure estimation. For diastolic blood pressure estimation, the model recorded an absolute mean error and standard deviation of 2.1190 mmHg and 3.1795 mmHg, respectively. These results surpass those obtained using other deep learning models, which are rooted in convolutional neural networks and recurrent neural networks. Our findings confirm that the proposed model significantly outperforms 10 comparative models in beat-by-beat blood pressure estimation tasks. Moreover, our study underscores the superiority of the TCN architecture over CNN, RNN, and hybrid CNN-RNN structures in tackling time series challenges. To further enhance the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**SBP Estimation Error**} & \multicolumn{3}{c}{**DBP Estimation Error**} \\ \cline{2-7}  & **MAE** & **STD** & **MAPE** & **MAE** & **STD** & **MAPE** \\ \hline Linear & 14.0665 & 17.9088 & 0.1097 & 4.5460 & 5.8711 & 0.0653 \\ SVR & 12.7897 & 16.3983 & 0.0990 & 4.5311 & 5.9229 & 0.0591 \\ MLP & 13.5818 & 17.3121 & 0.1039 & 4.3163 & 5.9181 & 0.0623 \\ XGBoost & 8.4072 & 11.9345 & 0.0656 & 2.9859 & 4.1835 & 0.0431 \\ RF & 7.1597 & 10.8400 & 0.0560 & 2.7073 & 3.9852 & 0.0391 \\ KNN & 7.3237 & 11.1455 & 0.0569 & 2.6817 & 4.0246 & 0.0386 \\ CNN & 8.2555 & 11.6321 & 0.0629 & 3.3077 & 4.3385 & 0.0336 \\ CNN-GRU & 5.6089 & 9.1172 & 0.0431 & 2.5339 & 3.7756 & 0.0368 \\ CNN-LSTM & 5.3837 & 9.0737 & 0.0420 & 2.5029 & 3.7069 & 0.0361 \\ TCN & 5.8680 & 8.8940 & 0.0405 & 2.3707 & 3.8023 & 0.0307 \\
**TCN-CBAM** & **5.3482** & **8.3410** & **0.0334** & **2.1190** & **3.1795** & **0.0240** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of model test results’ accuracy.  
model's performance, potential improvements could involve exploring more sophisticated attention mechanisms tailored specifically for physiological time series data, as well as investigating the integration of additional biometric parameters to enrich the feature space. Future research may also include the application of this model to real-time blood pressure monitoring systems, evaluating its performance on diverse populations and refining the model to handle noisy or irregularly sampled data, thereby increasing its robustness and generalizability in clinical settings.  
Conceptualization, D.D.; methodology, D.D.; software, D.D.; validation, D.D.; formal analysis, D.D.; investigation, D.D.; resources, D.D.; data curation, D.D.; writing--original draft preparation, D.D.; writing--review and editing, H.W.; visualization, D.D.; supervision, D.D. and H.W.; project administration, Z.J.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Key Program of the National Natural Science Foundation of China [grant number: 71531004]. Data are contained within the article. The authors would like to thank the Assistant Editor of this article and the anonymous reviewers for their valuable suggestions and comments. No potential conflicts of interest were reported by the author.