###### Abstract
In this study, we introduce a deep neural network architecture, named Neural Model of Blood Pressure (NeuBP), meticulously designed to automatically extract crucial features from PPG signals and estimate the ABP signal. The absence of manual feature extraction steps represents a significant advantage, simplifying and expediting the inference process while reducing the potential for human error. NeuBP incorporates dilated convolutional blocks, allowing for the effective and comprehensive capture of the relationships among these features, increasing the precision level in blood pressure estimation. In contrast to previous approaches, the evaluation of the proposed model addresses not only the binary question of the presence or absence of hypertension but also ventures into the multiclass classification of different blood pressure categories, including normotension, prehypertension, and hypertension of Grades 1 and 2. This expansion of the scope of analysis reflects the commitment to providing a robust and comprehensive tool for cardiovascular state assessment. The rigorous evaluation of NeuBP, conducted using data from the renowned MIMIC-III database, validates its effectiveness and reliability. Our experimental results demonstrate not only NeuBP's ability to rival more complex models, like the one proposed by [19], but also its potential to stand out as a viable and accessible solution for continuous blood pressure monitoring. Key contributions of this study encompass the following:  
* Automatic feature extraction: Unlike existing approaches that rely on manual feature extraction processes, Neural Model of Blood Pressure (NeuBP), introduced in this work, is a deep neural network architecture meticulously designed to automatically extract crucial features from PPG signals and estimate the ABP signal. The absence of manual feature extraction steps significantly simplifies and expedites the inference process while reducing the potential for human error. * Alignment of ABP and PPG signals through noise removal: We implement advanced signal processing techniques to effectively eliminate noise and ensure precise alignment between ABP and PPG signals. This synchronization of PPG-ABP segments, paired with the identification of blood pressure labels within the median of systolic and diastolic ABP segments, enhances the accuracy of our model. * Utilization of dilated convolutional blocks: NeuBP incorporates dilated convolutional blocks, allowing for the effective capture of relationships among features extracted from convolutional layers. This enhances the model's ability to discern complex patterns in the data, leading to improved precision in blood pressure estimation. * Comprehensive evaluation: Our experimental results not only demonstrate NeuBP's ability to rival more complex models but also its potential as a viable and accessible solution for continuous blood pressure monitoring. Additionally, our evaluation not only addresses the binary question of the presence or absence of hypertension but also ventures into multiclass classification of different blood pressure categories, reflecting our commitment to providing a robust tool for cardiovascular state assessment. The remainder of this article has the following structure: Section 2 explores related works. Next, we present our proposed method in Section 3. Section 4 details the experimentation, covering the training method, evaluation metrics, results, and discussions. Finally, Section 5 provides a comprehensive conclusion, summarizing key findings and outlining potential avenues for future research.