测控技术2024，Vol.43Issue(7)：23-30,70,9.DOI:10.19708/j.ckjs.2024.02.208
基于BiLRCN和注意力机制的脉搏波血压测量
Pulse Wave Blood Pressure Measurement Based on BiLRCN and Attention Mechanism
陈晓 1王志雄 2杨瑶2
作者信息
- 1. 南京信息工程大学电子与信息工程学院,江苏南京 210044||南京信息工程大学江苏省大气环境与装备技术协同创新中心,江苏南京 210044
- 2. 南京信息工程大学电子与信息工程学院,江苏南京 210044
- 折叠
摘要
Abstract
To improve the accuracy of noninvasive blood pressure measurement,a pulse wave blood pressure measurement method based on bidirectional long-term recurrent convolutional network(BiLRCN)and attention mechanism is proposed.The high-dimensional features of the photovolumetric pulse signal are extracted by two convolutional neural network(CNN),which is used as the input of bidirectional long short-term memory(BiL-STM)network,and the feature information in the forward and backward directions of the input sequence is ex-tracted by BiLRCN for prediction.The attention mechanism is used to automatically assign the weighted fea-tures,which gives a larger weight to the important moments of the pulse feature data,and the two fully connect-ed layers are used to obtain the blood pressure measurement value.The proposed method is compared with CNN,long short-term memory(LSTM)network,BiLSTM network,and long term recurrent convolutional neural network(LRCN)methods in terms of convergence speed and blood pressure measurement.The experimental re-sults show that the proposed method decreases the mean square error by 21.63％,decreases the mean absolute error by 67.5％,and improves the coefficient of deterministic correlation by 0.42％compared to LRCN.The proposed method has faster convergence and higher accuracy of blood pressure measurement.关键词
深度学习/脉搏波/血压测量/双向长期递归卷积网络/注意力机制Key words
deep learning/pulse wave/blood pressure measurement/BiLRCN/attention mechanism分类
信息技术与安全科学引用本文复制引用
陈晓,王志雄,杨瑶..基于BiLRCN和注意力机制的脉搏波血压测量[J].测控技术,2024,43(7):23-30,70,9.
