## 3 Results and Discussions
### Discussions  
Previous studies had shown that PAP can be estimated by heart sound features [28; 29; 30; 31; 32; 33]. [PERSON] [28] proposed to use neural network to estimate PAP in 1999 firstly and got standard estimated error 1.9 mmHg. The error was low. However, it was better to use cross-validation to test their model. After that, [PERSON] [19] extracted 38 features on different domains and used the feature selection method to select good features. They used cross validation methods and got a mean error of 8.3 mmHg, which may be the best performance at that time, to the authors' knowledge. Researchers [29; 30; 31; 32; 33] usually analyzed the relations between HS signals' time domain, frequency domain, amplitude domain, entropy domain features, and PAP. Then, they used machine learning to estimate the PAP. However, their focuses were on the feature's analysis. At the same time, the left ventricular blood pressure (LVBP) and aortic pressure were also researched by the investigators [34; 35; 36; 37]. The way to predict LVBP or aortic pressure was similar with the process of PAP prediction. They always separated the first and second heart sounds, known as S1 and S2, from the directly measured heart sound signals. However, precise automated localization of the S1 and S2 or measurement of the splitting interval between the components of the S2 had proved difficult. In summary, it could be found that the estimated performance was related to the extracted features directly. However, feature extraction or feature selection was still a big problem. This paper suggested a deep learning based on Bi-LSTM to estimate RVSBP using heart sound signals without complex feature extraction. Bi-LSTM is an indirection ally connected network with two layers of LSTM, it gives full consideration to the relationship between the current data and the data before and after it. Bi-LSTM solves the gradient disappearance or gradient explosion problem in RNN, and overcomes the drawback that LSTM only considers the relationship between the current data and its previous data. In this paper, we chose Bi-LSTM rather than LSTM; we thought that the prediction of the time series not only needs to consider the previous data but also the later data. So, we chose the Bi-LSTM. To illustrate the performance of the Bi-LSTM network, we performed multiple sets of experiments in the RVSBP estimation based on the HS signal. It was comprehensively evaluated by three schemes, i.e., inside trial (scheme I), across trials but within subject (scheme II), and across subjects (scheme III). Both scheme I and scheme II could get good estimated results. If we trained and tested the data between individuals directly, the effects were not good (such as \"scheme III Bi-LSTM\"), because of the individual differences between subjects. Individual difference means that when the basic conditions are the same, there will be significantly different reactions between individuals, such as high sensitivity reaction, low sensitivity reaction, and specific reaction. In this paper, the reasons for individual differences were extensive and complicated, mainly due to the absorption and metabolism of drugs being different for different subjects. The blood concentration of the same dose of drugs in a different individual is different, so that the intensity and duration of the drug action are very different for different individuals. In this paper, each individual was injected with the same dosage of the drug by the experiment operator. However, the subjects had different absorptions and metabolisms. Figure 9: Box plots of the model performance. (**a**) MAE1-MAE5 and SD1-SD5 of scheme I, (**b**) MAE6 and SD6 of scheme II, (**c**) MAE7-MAE9 and SD7-SD9 of scheme III. In Figure 10, the histograms of RVSBP distribution between individuals were shown to compare the RVSBP differences across subjects. The dark blue histogram represented RVSBP distribution. The red curve represented the fitted Gaussian distribution curve based on the RVSBP distribution histogram.