## 3 Results and Discussions
### Discussions
The fitting parameters (\(\mu\) and \(\sigma\)) were obtained. The Figure 10 showed the RVSBP value distributions of No.1, No.4, and No.5 were roughly from 20 to 50 mmHg. However, the rang of RVSBP value distributions of No.2 and No.3 were larger than the range of No.1, No.4, and No.5. Due to the individual differences, the authors added the corrective factors to correct errors between individuals. Finally, the estimations of RVSBP between different individuals got better estimated outcomes than before in scheme III. As shown in Figure 8, although the absolute value between the measured RVSBP and the estimated RVSBP differed, the relative value difference was small. The trend between the estimated RVSBP and the real RVSBP was very close. Such as No.1, No.3, No.4, and No.5 in Figure 8, the trends of estimated RVSBP were basically consistent with the trends of the measured RVSBP. Table 3 showed the regression coefficient. Combined the distribution of the RVSBP data and the regression coefficient distribution of the feature's analysis, we found that the higher of the distribution of RVSBP values, the higher of the weight of the S1_max. This finding maybe used as a trend to predict RVSBP. In our work, the Bi-LSTM network did not require complex feature extraction techniques and we could estimate the trend of RVSBP. Although our method could estimate the RVSBP non-invasively, we also discovered drawbacks from the wrong estimated samples. As shown in scheme II #20, #23, and #24 from Figure 7, the three records were from the subject 3. At the same time, we could see the outcome of the subject 3 in the scheme III which outcome was the worst in five subjects. It is probably due to this sample's range of RVSBP being large, so that the individual difference of the subject 3 was bigger than other subjects. Eventually, the outcome of the estimation of the subject 3 was worse than others. This problem may  
Figure 10: Histogram of RVSBP distribution. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{3}{c}{**Coefficient: a,b,c**} \\  & **a** & **b** & **c** \\ \hline
1 & 16.49 & \(-\)0.29 & \(-\)11.52 \\
2 & 30.42 & \(-\)0.70 & \(-\)4.36 \\
3 & 35.74 & \(-\)1.72 & 26.63 \\
4 & \(-\)3.10 & \(-\)0.95 & \(-\)1.15 \\
5 & \(-\)32.60 & 1.16 & \(-\)3.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of regression coefficients. be solved in two ways. On the one hand, the authors found the trends of the predicted results were good, but there was a constant between the measured RVSBP and the predicted RVSBP. If we could find the pathological mechanism or the relationship between the HS features and the RVSBP. Maybe the predicted results could compensate a constant and then the predicted results will be accurate. On the other hand, we need to add the number of the subjects. So that there were enough subjects, the model could learn and validate the relation fully. We need to reduce our gap between the different individuals and elevate the accuracy of the RVSBP forecasts in the future study. Besides, the RVSBP was influenced by breathing. So, the signal was slightly up and down, fluctuating during the process of collecting. Eventually the collected RVSBP signal was not smooth. In the next step, we need to try to solve these problems.