Mi Jiachen, Zhao Zehang, Wang Hongkai, Tang Hong
School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian 116024, China.
INTESIM (Dalian) Co., Ltd., Dalian 116024, China.
Bioengineering (Basel). 2023 Aug 20;10(8):985. doi: 10.3390/bioengineering10080985.
The vibration of the heart valves' closure is an important component of the heart sound and contains important information about the mechanical activity of a heart. Stenosis of the distal pulmonary artery can lead to pulmonary hypertension (PH). Therefore, in this paper, the relationship between the vibration sound of heart valves and the pulmonary artery blood pressure was investigated to contribute to the noninvasive detection of PH. In this paper, a lumped parameter circuit platform of pulmonary circulation was first set to guide the establishment of a mock loop of circulation. By adjusting the distal vascular resistance of the pulmonary artery, six different pulmonary arterial pressure states were achieved. In the experiment, pulmonary artery blood pressure, right ventricular blood pressure, and the vibration sound of the pulmonary valve and tricuspid valve were measured synchronously. Features of the time domain and frequency domain of two valves' vibration sound were extracted. By conducting a significance analysis of the inter-group features, it was found that the amplitude, energy and frequency features of vibration sounds changed significantly. Finally, the continuously varied pulmonary arterial blood pressure and valves' vibration sound were obtained by continuously adjusting the resistance of the distal pulmonary artery. A backward propagation neural network and deep learning model were used, respectively, to estimate the features of pulmonary arterial blood pressure, pulmonary artery systolic blood pressure, the maximum rising rate of pulmonary artery blood pressure and the maximum falling rate of pulmonary artery blood pressure by the vibration sound of the pulmonary and tricuspid valves. The results showed that the pulmonary artery pressure parameters can be well estimated by valve vibration sounds.
心脏瓣膜关闭时的振动是心音的重要组成部分，包含有关心脏机械活动的重要信息。肺动脉远端狭窄可导致肺动脉高压（PH）。因此，本文研究了心脏瓣膜振动音与肺动脉血压之间的关系，以有助于无创检测肺动脉高压。本文首先搭建了一个肺循环的集总参数电路平台，以指导建立模拟循环回路。通过调节肺动脉远端血管阻力，实现了六种不同的肺动脉压力状态。在实验中，同步测量了肺动脉血压、右心室血压以及肺动脉瓣和三尖瓣的振动音。提取了两个瓣膜振动音的时域和频域特征。通过对组间特征进行显著性分析，发现振动音的幅度、能量和频率特征有显著变化。最后，通过持续调节肺动脉远端阻力，得到了连续变化的肺动脉血压和瓣膜振动音。分别使用反向传播神经网络和深度学习模型，通过肺动脉瓣和三尖瓣的振动音来估计肺动脉血压、肺动脉收缩压、肺动脉血压最大上升速率和肺动脉血压最大下降速率的特征。结果表明，瓣膜振动音能够很好地估计肺动脉压力参数。
