基于多波长光电容积脉搏波的连续血压检测方法研究

Continuous blood pressure monitoring is predictable for cardiovascular mortality. However, the accuracy and stability of continuous blood pressure monitoring have not been solved in recent years. The difficulties lie in inaccurate calculation of pulse transmit time (PTT) and incomplete consideration of blood pressure related factors, especially the systemic vascular resistance (SVR). So far, there is no standard method to measure it. As a result, researchers have not considered it in the blood pressure calculation model. This proposal will deeply study a new continuous blood pressure detection method based on multi-wavelength Photoplethysmography (PPG). In this proposal, multi-wavelength PPG will be used to reflect the SVR. Combined with the existing theory, a multi-feature non-linear integrated model of continuous blood pressure will be constructed. Firstly, according to the detected multi-wavelength PPG and ECG signals, the PPG signals of arteries and capillaries will be reconstructed by individual dynamic adaptive algorithm. And the PTT of arterioles and arteries will be extracted accurately from them, eliminating the interference of different vessels to the signals. At the same time, the correlation mechanism between arteriole PTT and SVR will be analyzed. The arteriole PTT will be used to reflect the SVR. Finally, a multi-feature, non-linear integrated blood pressure calculation model will be constructed based on heart rate, arteriole PTT and artery PTT, to fully reflect the major factors affecting blood pressure. The purpose of the proposal is to implement a universal, high-precision, non-invasive and cuffless continuous blood pressure detection method.

连续血压监测对心血管疾病死亡率具有更强的预见性，但这几年对连续血压检测的研究仍未解决精准度和稳定性问题，难点在于脉搏波传导时间（PTT）计算方法不准确以及血压关联因素考虑不全面，尤其是外周血管阻力，至今没有测量其的标准方法，因而没有被研究者考虑到血压计算模型中去。本项目将深入研究基于多波长光电容积脉搏波（PPG）的新型血压检测方法，通过多波长PPG来反应外周血管阻力，再结合现有理论，构建多特征连续血压非线性集成模型。首先根据采集到的多波长PPG和ECG信号，通过个体动态自适应算法，重建动脉和毛细血管PPG信号，从中精确提取小动脉和动脉PTT，排除不同血管的相互干扰。同时分析小动脉PTT与外周血管阻力的关联机制，用小动脉PTT来表达外周血管阻力。最后构建基于心率、小动脉PTT和动脉PTT的多特征血压计算非线性集成模型，充分反应血压变化的关联因素，实现具有普适性的高精准无创无袖带连续血压检测。

血压是人体重要的生理指标之一，而高血压是心血管疾病的主要诱因。连续血压检测对于心血管疾病的死亡率具有更强的预见性。近些年连续血压检测的问题在于脉搏波传递时间（Pulse Transit Time, PTT）计算方法不准确以及血压关联因素考虑不全面，尤其是外周血管阻力（Systemic Vascular Resistance，SVR），至今没有体表连续测量的标准方法，因而几乎没有被研究者考虑到血压计算模型中去。所以，通过体表可测信号来追踪外周血管阻力对于连续血压检测有着重要意义。因此，本研究引入了多波长光电容积脉搏波（Photoplethysmography，PPG），提出了一种基于最小均方算法（Least Mean Square, LMS）的多波长PPG算法，从而提取小动脉脉搏波传递时间（arteriolar Pulse Transmit Time, aPTT）来跟踪外周血管阻力。 .然而该方法得到的小动脉PTT缺少实验证明，证明其可以反应外周血管阻力，因此本论文还提出了两者关系的实验验证方法。大量研究表明交感神经与高血压有着强烈的联系。交感神经的活动会通过收缩小动脉来影响血压，而小动脉的收缩会增加外周血管阻力。本研究设计冷刺激实验和情绪刺激实验，通过交感神经活动的变化验证小动脉PTT和外周血管阻力的关系。最后，本研究还将基于机器学习的方法构建连续血压检测模型，再在其中验证小动脉PTT对于连续血压计算的重要性。