融合注意力机制的BiLSTM网络实现无创血压测量  

Noninvasive blood pressure measurement based on BiLSTM network with attention mechanism

作　　者：陈晓[1,2] 杨瑶[1] Chen Xiao;Yang Yao(School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044 [2]南京信息工程大学大气环境与装备技术协同创新中心,南京210044

出　　处：《电子测量技术》2022年第23期59-65,共7页Electronic Measurement Technology

摘　　要：血压是人体的重要生理指标特征,血压的准确测量对心血管类疾病的诊断和治疗有重要意义。提出了基于注意力机制的双向长短记忆网络用于无创血压测量的方法。由于注意力机制可以根据特征的重要性来分配权重系数,所以方法把其引入到双向长短记忆网络中从脉搏波中计算出血压。试验结果表明引入注意力机制的双向长短记忆网络相较于双向长短记忆网络,MSE值和MAE值都得到了大幅度的降低,分别降低了18.29%和21.27%,R方值提高了0.17%。方法提高了无创血压测量的精度。blood pressure is an important physiological index of human body. It can judge the cardiovascular function and heart condition of the body. Many diseases are closely related to blood pressure. Therefore, the correct determination of blood pressure is of great significance for the diagnosis and treatment of cardiovascular diseases. We proposed a noninvasive blood pressure measurement method based on BiLSTM network. Firstly, taking the BiLSTM network and the traditional LSTM as the experimental model, and comparing the output evaluation index coefficients, it is found that the BiLSTM network has a better effect on blood pressure measurement. Because the attention mechanism can assign weight coefficients from rows according to the importance of features, it is introduced into the BiLSTM network with good measurement effect for experiments. According to the results, it is found that compared with the original BiLSTM model, the MSE value and Mae value of the introduced attention mechanism model are greatly reduced by 18.29% and 21.27% respectively, and the R-square value is increased by 0.17%.

关 键 词：血压测量 脉搏 双向长短记忆网络 注意力机制 

分 类 号：TN911.7[电子电信—通信与信息系统]