Pulse Wave Blood Pressure Measurement Based on BiLRCN and Attention Mechanism
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.
deep learningpulse waveblood pressure measurementBiLRCNattention mechanism
国家科技期刊平台
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