## 4 Experiments and Results
### Results and Discussion  
#### 4.3.1 Generalist Model (GM)  
The primary objective of the generalist evaluation was to develop a model based on data from a diverse set of individuals, encompassing the training, test, and validation sets. Figure 4 visually presents the distribution of data about predicted values (Pred) against the target values (True) for the systole (Sis) and diastole (Dias) classes, within the range of 40 mmHg to 200 mmHg. The distribution of true data for the systole category showed an interquartile range (IQR) in which the first quartile (Q1) was \(\approx\)104 mmHg, the second quartile (Q2) or median was very close to 120 mmHg, and the third quartile (Q3) was \(\approx\)139 mmHg. The interquartile range (IQR) \(\approx\)35 mmHg, and the minimum and maximum values corresponded to approximately 58 mmHg and 183 mmHg, respectively. Additionally, there were outliers both below the lower limit and above the upper limit. Next, the distribution of true data for the diastole category also showed symmetry in the data, with Q1 \(\approx\) 57 mmHg; Q2 = 60 mmHg, Q3 \(\approx\) 63 mmHg; IQR \(\approx\) 6 mmHg; and minimum and maximum values corresponding to 40 mmHg and 84 mmHg, respectively. Moreover, there were outliers above the upper limit of this distribution. Once the distribution of true data had been observed, the data predicted by NeuBP and the Spectro-Temporal [19] model helped us understand the ability of each model to reproduce the distributions of systole and diastole data in each of the presented experiments. Figure 4: Sys—systolic range; Dias—diastolic range; True—target values; Pred—predicted values. Dispersion analysis of values predicted by the GM experiment in relation to actual values in relation about NeuBP and Spectro-Temporal [31] models. When observing the predictions made by our model, NeuBP, for the systole category, it is evident that the model maintained the symmetry of true data while presenting lower dispersion, resulting in quartiles Q1 = 118 mmHg, Q2 \(\approx\) 123 mmHg, and Q3 \(\approx\) 130, showing very close matching, resulting in an IQR interval of \(\approx\)12 mmHg. As the minimum and maximum values also followed the flattening of dispersion, many outliers could be noticed, but still within the interval present in the true data. Next, when evaluating the same systole category predicted by the Spectro-Temporal model, the box plot showed positive symmetry, the same Q2 value concerning the true median, and an IQR dispersion of \(\approx\)20 mmHg; however, there were many outliers above the upper limit. When observing the predicted values of the diastole category in relation to the true values, we saw that both models also presented less dispersion: NeuBP with an IQR value of \(\approx\)7 mmHg and the Spectro-Temporal model with an IQR of \(\approx\)8 mmHg. Both pointed to a Q2 value of 60 mmHg, and the Spectro-Temporal model presented more outliers than NeuBP. Table 1 presents key metrics, including mean, standard deviation, and mean absolute error (MAE), offering a comparative assessment of NeuBP and the Spectro-Temporal model against the test dataset's standard deviation and mean. Notably, NeuBP achieved an MAE of 17.69 mmHg for systole and 8.90 mmHg for diastole on the test set. The Table 1 comprehensively displays statistics for systolic and diastolic points from the test dataset, alongside corresponding predictions made by the models in this study. To evaluate the alignment of predicted values with hypertension classes, two classification steps were implemented, conditioning the values to blood pressure intervals defined by the International Hypertension Association [3]. These steps involved (1) classification into four classes (Normotensive, Prehypertensive, Hypertensive Stage 1, and Hypertensive Stage 2) and (2) binary classification (Normotensive and Hypertensive). Figure 5 presents the multiclass confusion matrix and its associated metrics. Both models exhibited a bias towards the Normotensive category, aligning with the median described in the block diagrams.