最新研究表明，除血压的高低水平，血压的动态变化也是心脑血管疾病非常重要的风险因子。当前利用心电和单路脉搏波,间接估计连续血压，无需袖带，能方便地提供血压动态信息。然而，受血管弹性因素影响，估计精度不高。本项目提出基于双路脉搏波建模的无袖带连续血压估计方法。脉搏波在动脉传播过程中的形变受血管弹性影响，协同分析动脉不同位置的双路脉搏波有望减少血管弹性对血压估计精度的影响。拟利用柔性传感获取的肱、桡动脉双路脉搏波，建立基于机制传导的脉搏波传播模型（建模1），利用系统辨识解析模型参数，探明双路脉搏波的形变和时延与血压的映射关系；建立基于数据驱动的神经网络模型（建模2），利用机器学习估计模型参数，探明脉搏波波形/特征与血压的内在联系，实现无袖带连续血压估计。对比两种模型，找出适合个体的最优血压估计方法。本项目有望开发无袖带连续血压估计的新方法，提高连续血压估计精度，对心脑血管疾病的防治具有重要意义。

Current researches show that besides the level of blood pressure, the dynamic change of blood pressure is also a very important risk factor of cardiovascular and cerebrovascular diseases. At present, continuous blood pressure can be estimated indirectly by ECG and single pulse wave without using a cuff, which could provide dynamic information of blood pressure conveniently. However, this method is affected by factors such as vascular elasticity, and the estimation accuracy is limit. This project proposed a cuff-less and continuous blood pressure estimation based on dual pulse waves modeling. The morphological changes of pulse wave while transmitting in the artery are affected by the vascular elasticity, so it is expected to reduce the influence of vascular elasticity on the blood pressure estimation accuracy by collaborative analysis of dual pulse wave recordings in two different positions of artery. Pulse waves in brachial artery and radial artery will be collected by flexible sensors, a mechanism-driven pulse wave propagation model will be developed (modeling 1), and system identification technology would be used to estimate the model parameters for exploring the mapping relationship of the pulse waves morphological changes and the time delay to blood pressure, while, a data-driven neural network model will be developed (modeling 2), and machine learning technology would be used to estimate the model parameters for exploring the internal relationship between the pulse wave waveform/characteristics and blood pressure. So that, cuff-less and continuous blood pressure could be estimated by using dual pulse waves. Comparing the two modeling methods to find out the best blood pressure estimation method for individual. This project is expected to develop a new cuff-less and continuous blood pressure estimation method to improve the accuracy of cuff-less and continuous blood pressure estimation, which is of great significance for the prevention and treatment of cardiovascular and cerebrovascular diseases.