Chen Xiao
1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, ChinaYang Yao
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China1.School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China 2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
TN911.7
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%.
