## 1 Introduction  
Information about the role of the right ventricle (RV) in health and disease historically had fallen behind that of the left ventricle (LV) [1; 2]. Comparatively, little attention had been paid on how RV dysfunction may be detected and measured efficiently. RV function may be impaired in pulmonary hypertension (PH), congenital heart disease, and coronary artery disease and in patients with left-sided heart failure or valvular heart disease [3; 4; 5; 6]. Previous studies had shown that the potential estimated value of right ventricular blood pressure (RVBP) for patients with right heart failure was needed [7]. Right ventricular systolic blood pressure (RVSBP) is a critical and indispensable indicator for cardiovascular diseases, especially for early discovery and postoperative observation of PH. In the early disease, the symptoms of PH were often non-specific but progressed over time to functionally limiting dyspnea and fatigue. The non-specific symptoms in the early disease were some of the obstacles to establishing an early diagnosis [8]. Although the natural history varied according to the etiology of the condition, PH was usually a progressive disease characterized by increasedpulmonary vascular resistance and diminished RV function due to increased RV afterload. The normal RV is a thin-walled flow generator able to accommodate large changes in venous return but unable to maintain flow output when a fast increase in pulmonary artery pressure (PAP). Therefore, RVBP increased with the development of PH. In this paper, the authors extracted the RVBP instead of PAP. As far as the author's knowledge, there were no literatures about the prediction of RVBP. The authors compared the prediction of RVBP with the prediction of PAP. Right heart catheterization, an invasive procedure, is usually mandatory to confirm the diagnosis of right ventricular disease [9]. However, the financial expense and physical risk of right heart catheterization discourages patients from diagnosis and treatment. Moreover, the right heart catheterization is placed into the heart through a catheter. It is not fit to monitor RVBP or PAP over a long period of time. In clinical practice, there are two possible solutions to diagnose RV diseases non-invasively: Doppler echocardiography and phonocardiography [10]. Doppler echocardiography er evaluates the speed of tricuspid regurgitation to calculate PAP [11]. However, some recent studies had suggested that Doppler echocardiographic may frequently be inaccurate [12]. Usually, patients' diagnosis began with a detailed medical history collection and series of physical examinations in hospital. Systematic chest auscultation was crucial for early screening. Heart sound (HS) auscultation was widely used in routine examinations as a noninvasive and low-cost method. In earlier years, researchers found there were relations between the A2-P2 time split interval (TSI), which was further used to estimate PAP based on signal processing of the second heart sounds (S2s) [13; 14; 15; 16; 17]. However, the error of the TSI detection affected PAP estimation directly. This limitation prevented the accuracy of PAP estimation. Subsequently, researchers extracted feature sets from the heart sounds and used machine learning algorithms to setup predictive models for PAP estimation [18; 19; 20]. Features could be extracted from a wide variety of signal transforms, such as short time Fourier transform (STFT), wavelet transform, S-transform, etc. However, feature extraction was a challenging task. These handpicked features had limitations because they were selected based on a human being's subjective analysis. [PERSON] and [PERSON], et al., built the models on human data. The standard deviation (SD) of the best general model were, respectively, 11.7 mmHg and 8.28 mmHg for PAP estimation using heart sounds [18; 19]. As technology advances, new approaches need to be tried. The problem is still open. Deep learning had shown strong ability in modelling nonlinear relations from input to output [21].