## 2 Related Works  
In the literature, distinct research methodologies for processing and classifying hypertension via PPG have emerged. These approaches can be broadly categorized into two prevalent paradigms for predicting systolic and diastolic values. The first paradigm relies on the application of classical models that utilize statistical data extracted from both public and private datasets. In contrast, the second approach is characterized by studies employing deep learning techniques for the automatic extraction of temporal features from PPG time series. [PERSON] and [PERSON] [25] introduced a support vector machine (SVM) approach to estimate ABP using PPG signals and the UQVS database. They partitioned the dataset into training and test subsets, maintaining a non-personalized separation by avoiding data overlap from the same patient in both sets. The evaluation of blood pressure relied on systolic and diastolic points, with metrics including absolute error and relative error. The reported results for systole and diastole were 11.641 \(\pm\) 8.202 mmHg and 7.617 \(\pm\) 6.783 mmHg, respectively. [PERSON] et al. [26] proposed the evaluation of models for estimating ABP in real time based on linear regression, neural networks, decision trees, bagging, and random forests. The experiments used the University of Queensland Vital Signs (UQVS) database [27], consisting of 11,492 PPG signal records. A random forest model attained errors of 7.59 mmHg \(\pm\) 5.39 mmHg in DBP and 4.21 mmHg \(\pm\) 3.24 mmHg in SBP (absolute mean \(\pm\) standard deviation). [PERSON] et al. [28] presented a study on continuous blood pressure estimation using PPG signals. They applied a neural network algorithm to predict systolic and diastolic values, utilizing data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database [29]. Validation involved a holdout approach with data being split as 70:15:15 into training, test, and validation sets, respectively. The neural model achieved mean absolute error (MAE) and standard deviation values for systole and diastole of 3.80 \(\pm\) 3.46 mmHg and 2.21 \(\pm\) 2.09 mmHg, respectively. It is important to note the utilization of a small undisclosed subset of the MIMIC database, comprising over 150,000 cycles of PPG signals with corresponding blood pressure defined by the ABP signal. [PERSON] et al. [30] proposed the use of deep learning methods to classify blood pressure levels from PPG signals, aiming to detect cardiovascular diseases associated with hyper tension in an early stage. They employed data from 121 patients in the publicly available MIMIC III database [29] and transformed temporal data into scalograms using Continuous Wavelet Transform (CWT). During model training, the authors applied transfer learning from the GoogLeNet model to classify ABP levels into Normotensive (NT), Prehypertensive (PHT), and Hypertensive (HT) categories, evaluated using the F1-Score metric. The results demonstrated accuracy of 80.52% for NT versus PHT, 92.55% for NT versus HT, and 82.95% for NT + PHT versus HT. [PERSON] et al. [31] devised a method for estimating blood pressure from PPG signals using machine learning algorithms to select relevant features from statistical data extracted in the time and frequency domains of the PPG signal. The authors employed two configurations for validating their method: k-fold cross-validation (with five subsets) and Leave One Subject Out (LOSO). In the 5-fold validation, the random forest model yielded the best results, with a mean error of 4.90 \(\pm\) 6.59 mmHg in SBP and 2.21 mmHg \(\pm\) 3.70 mmHg in DBP. In the LOSO validation, the values were 4.47 \(\pm\) 5.85 mmHg for systole and 2.02 mmHg \(\pm\) 2.94 mmHg for diastole. [PERSON] et al. [19] achieved outstanding results by using the explored Spectro-Temporal method for blood pressure estimation on MIMIC-III data [29]. They employed a deep neural network model with three input channels (pure PPG signal, first derivative, and second derivative) in 5 s windows and 786.875 parameters.