## 5 Conclusions  
In this article, we propose NeuBP, a blood pressure estimation method based on PPG, employing a 1D convolutional architecture with dilation mechanisms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Dataset** & **Input** & **Results** \\ \hline [PERSON] and [PERSON] [25] & 2017 & UQVS & PPG’s Feat. & SBP = 11.641 \(\pm\) 8.202; \\  & & & DBP = 7.617 \(\pm\) 6.783 \\ \hline [PERSON] et al. [26] & 2018 & UQVS & PPG’s Feat. & SBP = 4.21 \(\pm\) 3.24; \\  & & & DBP = 7.59 \(\pm\) 5.39 in \\ \hline [PERSON] et al. [2] & 2021 & MIMIC-III & PPG & SBP = 3.68; DBP = 2.17 \\ \hline [PERSON] et al. [32] & 2021 & MIMIC-III & PPG & SBP = 4.05; DBP = 2.41 \\ \hline [PERSON] et al. [33] & 2021 & MIMIC-III & PPG & SBP = 4.11; DBP = 7.95 \\ \hline [PERSON] et al. [34] & 2022 & MIMIC-III & PPG & SBP = 2.29; DBP = 1.93 \\ \hline NeuBP & 2024 & MIMIC-III & PPG & SBP = 5.02; DBP 3.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison among different approaches was conducted, considering well-established related works. Emphasis was placed on the publication year; dataset utilized; input types (raw signal or features extracted); and the outcomes, specifically the errors in mmHg generated for SBP and DBP. Figure 12: Confusion matrix of the values predicted in the SM experiment in relation to the true values in the binary classes, highlighting the best achieved among the analyzed models. The results obtained demonstrate the potential of the NeuBP method in the regression task for blood pressure estimation. The application of filtering methods on the PPG signal, outlier attenuation, and extraction of labels from the ABP signal were crucial to achieving competitive results compared with the literature. Thus, predictions were evaluated using regression-related error metrics and classification-linked metrics. The strategy of employing a neural model with a core composed of dilation blocks with 1D convolutional layers allows for an increase in the scale of features extracted by the model. This approach utilizes a less complex model capable of identifying changes in physiological signals captured by PPG to determine if the individual is affected by arterial hypertension through their variations. As a result, NeuBP demonstrates promise for the blood pressure regression task, indicating greater feasibility for its use in real-world scenarios due to its less complex nature. In the future, we intend to investigate the generalization capabilities of blood pressure estimation models across diverse datasets obtained from different populations, healthcare settings, or sensor modalities to assess their robustness and applicability in varied contexts. Additionally, we will assess the interpretability and explainability of blood pressure estimation models by employing techniques such as attention mechanisms or feature importance analysis. This approach establishes the groundwork for incorporating our solution into devices such as smartphones and wearables, with the goal of bolstering user confidence and streamlining clinical decision making in the context of hypertension detection. Conceptualization, H.S.D.O. and E.J.P.S. ; methodology, E.J.P.S. and R.A.P. ; validation, R.G. ; investigation H.S.D.O. and R.A.P. ; resources, H.S.D.O. ; data curation, H.S.D.O. ; writing--original draft preparation, H.S.D.O. and R.A.P. ; writing--review and editing, H.S.D.O., E.J.P.S. and R.A.P. and supervision E.J.P.S. All authors have read and agreed to the published version of the manuscript. This research was partially funded by Samsung Electronics of Amazonia Ltda, under the terms of Federal Law No. 8.387/1991, through agreement No. 003/2019, in accordance with Article 48 of Decree No. 6.008/2006. The study also received financial support from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES)-Finance Code 001, and the Amazonas State Research Support Foundation--FAPEAM--through the POSGRAD project. Not applicable. **Data Availability Statement:** Data is contained within the article.