基于深度学习的心血管血流动力学参数反演  

Parameter inversion for cardiovascular hemodynamics based on deep learning

作　　者：周阳 潘怡 崔畅 陈明龙[2] 孙蓓蓓[1] Zhou Yang;Pan Yi;Cui Chang;Chen Minglong;Sun Beibei(School of Mechanical Engineering,Southeast University,Nanjing 211189,China;Internal Medicine-Cardiovascular Department,The First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China)

机构地区：[1]东南大学机械工程学院,南京211189 [2]南京医科大学第一附属医院心血管内科,南京210029

出　　处：《东南大学学报（自然科学版）》2022年第2期394-401,共8页Journal of Southeast University：Natural Science Edition

基　　金：国家重点研发计划资助项目(2019YFB2006404)。

摘　　要：考虑现有血流动力学参数反演方法在实际应用中存在计算量大、迭代易发散等问题,提出一种新的基于深度学习的心血管血流动力学参数反演方法.首先建立一维-零维耦合的多尺度血流动力学模型;随后基于卷积神经网络和全连接神经网络提出一种用于参数反演的混合多源输入深度网络模型;针对测量波形中的噪声干扰问题,提出一种同时利用多个深度网络的集成网络模型以提高反演精度.在参数灵敏度分析的基础上,对所提方法进行参数反演实验,研究在不同噪声水平下的反演精度,并与卡尔曼滤波法进行比较.结果表明,血压与血流波形的预测误差显著低于已有方法.所提方法能够准确高效地实现心血管模型参数反演,具有较好的应用前景.Considering that the existing hemodynamic parameter inversion methods have some problems in practical application,such as a large amount of calculation and easy divergence of iteration,a new cardiovascular hemodynamic parameter inversion method based on deep learning was proposed.Firstly,a multi-scale hemodynamic model coupling one-dimensional model and zero-dimensional model was established.Then,based on convolution neural network and fully connected neural network,a hybrid multi-source-input deep network model for parameter inversion was proposed.Aiming at the problem of noise interference in measurement waveforms,an integrated network model using multiple depth networks was proposed to improve the inversion accuracy.Based on the parameter sensitivity analysis,the parameter inversion experiments of the proposed method were carried out to study the inversion accuracy under different noise levels,and they are compared with those obtained by Kalman filter method.The results show that the prediction errors of blood pressure and blood flow waveform are significantly lower than those of the existing methods.The proposed method can accurately and efficiently realize the parameter inversion of the cardiovascular model,and shows a good application prospect.

关 键 词：心血管 血流动力学 参数反演 深度学习 集成网络模型 

分 类 号：R318.01[医药卫生—生物医学工程]