Mi Jiachen, Feng Tengfei, Wang Hongkai, Pei Zuowei, Tang Hong
School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.
Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian 116024, China.
Bioengineering (Basel). 2024 Aug 19;11(8):842. doi: 10.3390/bioengineering11080842.
Beat-by-beat monitoring of hemodynamic parameters in the left ventricle contributes to the early diagnosis and treatment of heart failure, valvular heart disease, and other cardiovascular diseases. Current accurate measurement methods for ventricular hemodynamic parameters are inconvenient for monitoring hemodynamic indexes in daily life. The objective of this study is to propose a method for estimating intraventricular hemodynamic parameters in a beat-to-beat style based on non-invasive PCG (phonocardiogram) and PPG (photoplethysmography) signals. Three beagle dogs were used as subjects. PCG, PPG, electrocardiogram (ECG), and invasive blood pressure signals in the left ventricle were synchronously collected while epinephrine medicine was injected into the veins to produce hemodynamic variations. Various doses of epinephrine were used to produce hemodynamic variations. A total of 40 records (over 12,000 cardiac cycles) were obtained. A deep neural network was built to simultaneously estimate four hemodynamic parameters of one cardiac cycle by inputting the PCGs and PPGs of the cardiac cycle. The outputs of the network were four hemodynamic parameters: left ventricular systolic blood pressure (SBP), left ventricular diastolic blood pressure (DBP), maximum rate of left ventricular pressure rise (MRR), and maximum rate of left ventricular pressure decline (MRD). The model built in this study consisted of a residual convolutional module and a bidirectional recurrent neural network module which learnt the local features and context relations, respectively. The training mode of the network followed a regression model, and the loss function was set as mean square error. When the network was trained and tested on one subject using a five-fold validation scheme, the performances were very good. The average correlation coefficients (CCs) between the estimated values and measured values were generally greater than 0.90 for SBP, DBP, MRR, and MRD. However, when the network was trained with one subject's data and tested with another subject's data, the performance degraded somewhat. The average CCs reduced from over 0.9 to 0.7 for SBP, DBP, and MRD; however, MRR had higher consistency, with the average CC reducing from over 0.9 to about 0.85 only. The generalizability across subjects could be improved if individual differences were considered. The performance indicates the possibility that hemodynamic parameters could be estimated by PCG and PPG signals collected on the body surface. With the rapid development of wearable devices, it has up-and-coming applications for self-monitoring in home healthcare environments.
逐搏监测左心室血流动力学参数有助于心力衰竭、心脏瓣膜病和其他心血管疾病的早期诊断与治疗。当前用于心室血流动力学参数的精确测量方法在日常生活中监测血流动力学指标时并不方便。本研究的目的是提出一种基于无创心音图（PCG）和光电容积脉搏波描记法（PPG）信号以逐搏方式估计心室内血流动力学参数的方法。使用三只比格犬作为研究对象。在静脉注射肾上腺素以产生血流动力学变化的同时，同步采集PCG、PPG、心电图（ECG）和左心室有创血压信号。使用不同剂量的肾上腺素来产生血流动力学变化。共获得40组记录（超过12,000个心动周期）。构建了一个深度神经网络，通过输入心动周期的PCG和PPG信号来同时估计一个心动周期的四个血流动力学参数。该网络的输出为四个血流动力学参数：左心室收缩压（SBP）、左心室舒张压（DBP）、左心室压力上升最大速率（MRR）和左心室压力下降最大速率（MRD）。本研究构建的模型由一个残差卷积模块和一个双向循环神经网络模块组成，分别用于学习局部特征和上下文关系。网络的训练模式遵循回归模型，损失函数设置为均方误差。当使用五折交叉验证方案在一个研究对象上对网络进行训练和测试时，性能非常好。SBP、DBP、MRR和MRD的估计值与测量值之间的平均相关系数（CC）通常大于0.90。然而，当用一个研究对象的数据训练网络并用另一个研究对象的数据进行测试时，性能有所下降。SBP、DBP和MRD的平均CC从超过0.9降至0.7；然而，MRR具有更高的一致性，平均CC仅从超过0.9降至约0.85。如果考虑个体差异，跨研究对象的泛化能力可以得到提高。该性能表明通过体表采集的PCG和PPG信号估计血流动力学参数的可能性。随着可穿戴设备的快速发展，其在家庭医疗保健环境中的自我监测方面具有广阔的应用前景。
