## 4 Experiments and Results
### Results and Discussion
#### 4.3.2 Specialist Model with Retraining (SMR)  
For this evaluation scenario, the data split was 80:10:10 for training, validation, and testing, respectively. Initially, 50 users from the MIMIC-III database were selected; however, only 46 users were included in the experiment, meeting the minimum requirement of 9 samples or 63 s for testing and validation. It is essential to note that specialist models were generated based on the pre-trained model from the previous scenario, which constituted the generalist model. Regarding both NeuBp and the Spectral Temporal model, the dispersions about true and predicted outcomes are shown in Figure 7. The models closely matched the test data distribution pattern, as depicted in Figure 7. Given that the models run on data with consistent patterns within training and test subsets, all of which belong to a single user, this alignment was expected. The true dataset for the systole class showed a slight, positive asymmetry, where the median was closer to Q1. It exhibited Q1 = 102 mmHg; Q2 \(\approx\) 116 mmHg; Q3 \(\approx\) 139 mmHg; IQR \(\approx\) 37 mmHg; and lower and upper limits of \(\approx\)169 mmHg and \(\approx\)191 mmHg, respectively, along with some outliers in its upper limit. For this category, NeuBP presented identical values for Q1 and Q2, a smaller Q3 of \(\approx\)137 mmHg, and an IQR of 37 mmHg. The lower and upper limits practically had the same value, showing a predicted data dispersion very close to true data. On the other hand, the Spectro-Temporal model for the systole category also showed predicted values close to the true data, with only a slightly smaller lower tail and more pronounced outliers compared with the upper tail of the box plot. The same analysis results were observed for the diastole category. Our model, NeuBP, showed a dispersion very close to the true data, with a predicted Q1 of \(\approx\)65 mmHg, compared with the true Q1 of \(\approx\)63 mmHg. Both predicted and true values of Q2 and Q3 overlapped, where Q2 was \(\approx\)61 mmHg and Q3 was \(\approx\)7 mmHg, resulting in an IQR of \(\approx\)7 mmHg. When evaluating the Spectro-Temporal model, this model presented a predicted median (Q2) of approximately 1 mmHg above the true values, as well as a slightly higher Q3, with an IQR of \(\approx\)8 mmHg. The model also showed more pronounced outliers compared with the upper tail of the box plot. The mean, standard deviation, and MAE values in Table 2 indicate a notable reduction in error values for both models, with NeuBP exhibiting a superior MAE due to its smaller value. Concerning the predicted values for systole, Spectro-Temporal closely approximated NeuBP. Conversely, Spectro-Temporal demonstrated a closer approximation to diastolic values. Examining the means showed that both models presented identical integer values. Figure 7: Sys—systolic range; Dias—diastolic range; True—target values; Pred—predicted values. Dispersion analysis of the values predicted in the SMR experiment in relation to the true values. As depicted in Figure 8, the Normotensive category showed the highest number of data, causing an imbalance in the test dataset. Nevertheless, the models successfully distinguished signals from this class, facilitating accurate binary classification between Normotensive and Hypertensive. As shown in Figure 9, both models achieved much more optimistic performance, obtaining equality in the F1-Score metric and similarities in the others. It is important to note that the experiments with the specialist models with NeuBP and Spectro-Temporal started from a general pre-training. Therefore, there is a need to evaluate the performance of a specialist model without any pre-training, in order to determine the best approach for applying a methodology with a specialist model.