## 4 Experiments and Results
### Results and Discussion
#### 4.3.3 Specialist Model (SM)  
In the SM experiment, neural models were created without pre-training and adjusted to a specific individual. In practice, models with random weights were created without any adjustments, subjected to data from a single individual in the training, validation, and testing stages. Figure 10 illustrates the behavior of the models with the predicted values in relation to the systolic and diastolic distribution labels. It can be observed that both models faced difficulties, with many scattered values in their predictions, hindering the correlation of labels with blood pressure categories.  
Regarding the previous experiment, the data presented in Figure 10 that vary correspond only to the predicted values, since the models were not pre-trained.  
In this context, for the predicted data of the systole category, our model exhibited the following dispersion: Q1 \(\approx\) 103 mmHg, Q2 \(\approx\) 113 mmHg, Q3 \(\approx\) 136 mmHg, and IQR \(\approx\) 33 mmHg. It is also noticeable that there were outliers beyond both the upper limit and the lower limit. The Spectro-Temporal model maintained the following dispersion of data: Q1 \(\approx\) 100 mmHg, Q2 \(\approx\) 112 mmHg, and Q3 \(\approx\) 136 mmHg, along with an IQR \(\approx\) 36 mmHg. It can be observed that the data predicted by the Spectro-Temporal model had many outliers at both ends of the box plots. The same can be observed for the diastole category, where both models presented many outliers, and the Spectro-Temporal model  
Figure 10: Sys—systolic range; Dias—diastolic range; True—target values; Pred—predicted values. Dispersion analysis of the values predicted in the SM experiment in relation to the true values.  
Figure 9: Confusion matrix of the values predicted in the SMR experiment in relation to the true values in the binary classes, highlighting the best achieved among the analyzed models.  
exhibited a larger dispersion concerning the true data, performing worse than NeuBP in this aspect.  
Even though the models exhibited low values for the evaluation metrics, NeuBP managed to achieve a standard deviation and mean more correlated with the labels, and it also presented a lower MAE, as shown in Table 3.  
Although the focus of this experiment was to generate specialized models, there is a notable gap between this experiment and the SMR experiment. Training a specialist model without pre-training may require more samples to adjust the model to individual data points. However, NeuBP demonstrated better performance when applying the F1-Score metric to the Normotensive and Hypertensive Stage 1 classes, as shown in Figure 11.  
Finally, when visualizing the distribution of the predicted labels from a binary perspective, as shown in Figure 12, the NeuBP model achieved better performance, particularly in the F1-Score metric, failing to outperform Spectro-Temporal only in precision for the Hypertensive class.  
Figure 11: Confusion matrix of hypertension classes achieved with values predicted by NeuBP and Spectro-Temporal model. The best values among the models is highlighted.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Target**} & \multicolumn{2}{c}{**NeuBP**} & \multicolumn{2}{c}{**Spectro-Temporal**} \\ \hline  & **Systole** & **Diastole** & **Systole** & **Diastole** & **Systole** & **Diastole** \\ \hline Standard deviation & 23.18 & 12.99 & 19.35 & 10.99 & 30.23 & 17.02 \\ \hline Mean & 116.0 & 62.0 & 115.36 & 62.25 & 111.72 & 61.45 \\ \hline MAE [mmHg] & - & - & 11.03 & 6.30 & 16.00 & 9.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Arithmetic mean, standard deviation and absolute mean error in the SM experiment.