Objective: Timely monitoring right ventricular systolic blood pressure (RVSBP) is helpful in the early detection of pulmonary hypertension (PH). However, it is not easy to monitor RVSBP directly. The objective of this paper is to develop a deep learning technique for RVSBP noninvasive estimation using heart sound (HS) signals supported by (electrocardiography) ECG signals without complex features extraction. Methods: Five beagle dog subjects were used. The medicine U-44069 was injected into the subjects to induce a wide range of RVSBP variation. The blood pressure in right ventricle, ECG of lead I and HS signals were recorded simultaneously. Thirty-two records were collected. The relations between RVSBP and cyclic HS signals were modeled by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Results: The mean absolute error (MAE) ± standard deviation (SD) inside record was 1.85 ± 1.82 mmHg. It was 4.37 ± 2.49 mmHg across record but within subject. The corrective factors were added after training the Bi-LSTM network across subjects. Finally, the MAE ± SD from 12.46 ± 6.56 mmHg dropped to 6.37 ± 4.90 mmHg across subjects. Significance: Our work was the first to apply the Bi-LSTM network to build relations between the HS signal and RVSBP. This work suggested a noninvasive and continuous RVSBP estimation using the HS signal supported by the ECG signal by deep learning architecture without the need of healthcare professionals.

中文翻译：

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深度双向LSTM网络使用心音信号连续无创估计右心室收缩压

目的：及时监测右心室收缩压（RVSBP）有助于早期发现肺动脉高压（PH）。但是，直接监视RVSBP并不容易。本文的目的是开发一种使用（心电图）ECG信号支持的心音（HS）信号进行RVSBP无创估计的深度学习技术，而无需提取复杂的特征。方法：使用五只比格犬受试者。将药物U-44069注入受试者体内，以诱导各种RVSBP变化。同时记录右心室的血压，I导联的ECG和HS信号。收集了32条记录。RVSBP和循环HS信号之间的关系是通过双向长期短期记忆（Bi-LSTM）网络建模的。结果：记录内的平均绝对误差（MAE）±标准偏差（SD）为1.85±1.82 mmHg。整个记录为4.37±2.49 mmHg，但在受试者体内。在跨受试者训练Bi-LSTM网络后，添加了校正因子。最后，整个受试者的MAE±SD从12.46±6.56 mmHg下降至6.37±4.90 mmHg。意义：我们的工作是第一个应用Bi-LSTM网络在HS信号和RVSBP之间建立关系的工作。这项工作建议通过深度学习架构使用ECG信号支持的HS信号进行无创且连续的RVSBP估计，而无需医疗保健专业人员。46±6.56 mmHg降至受试者的6.37±4.90 mmHg。意义：我们的工作是第一个应用Bi-LSTM网络在HS信号和RVSBP之间建立关系的工作。这项工作建议通过深度学习架构使用ECG信号支持的HS信号进行无创且连续的RVSBP估计，而无需医疗保健专业人员。46±6.56 mmHg降至受试者的6.37±4.90 mmHg。意义：我们的工作是第一个应用Bi-LSTM网络在HS信号和RVSBP之间建立关系的工作。这项工作建议通过深度学习架构使用ECG信号支持的HS信号进行无创且连续的RVSBP估计，而无需医疗保健专业人员。