%0 Journal Article
%T 基于脉搏波和小波神经网络的血压预测
%A 刘艳萍
%A 李
%A 杰
%A 金
%A 菲
%A 崔
%A 彤
%J 河北工业大学学报
%D 2018
%R 10.14081/j.cnki.hgdxb.2018.03.003
%X 为了进一步提高血压的预测精度，实现无创连续血压测量. 本文提出了一种基于脉搏波和小波神经网络 的血压预测算法，通过脉搏波采集系统采集不同人的脉搏波信号，采用椭圆低通滤波器去除高频噪声，并且 有效提取出脉搏波中关于血压的相关特征，建立BP神经网络和小波神经网络预测模型，不断优化学习速率以 及隐含层层数，从而可以准确地预测出血压值. 结果表明，建立的脉搏波采集系统可以采集到信噪比较高的人 体脉搏波信号；建立的BP神经网络和小波神经网络模型，实现了对血压的无创连续预测，并且通过对比血压 预测的误差验证了小波神经网络比BP神经网络的预测效果更佳.</br>Inordertofurtherimprovethepredictionaccuracyofbloodpressure,andachievenoninvasivecontinuous bloodpressuremeasurement,abloodpressurepredictionalgorithmbasedonpulsewaveandwaveletneuralnetworkis proposedinthispaper.Thepulsewaveacquisitionsystemisusedtocollectthepulsewavesignalofdifferentpeople,and thehighfrequencynoiseisremovedbyellipticlowpassfilter,andtherelatedcharacteristicsofbloodpressureareex? tractedinaneffectiveway.TheBPneuralnetworkandthewaveletneuralnetworkpredictionmodelareestablishedtoop? timizethelearningrateandthenumberofhiddenlayers,sothatthebloodpressurevaluecanbeaccuratelypredicted. Theresultsshowthattheestablishedpulsewaveacquisitionsystemcancollectthehumanpulsewavesignalwithhigh signal-to-noiseratio.TheBPneuralnetworkandthewaveletneuralnetworkmodelareusedtocontinuouslypredictthe bloodpressureinanon-invasivewayandcomparetheerrorofbloodpressureprediction.Itisprovedthatthewavelet neuralnetworkisbetterthanBPneuralnetwork.
%K 脉搏波
%K BP神经网络
%K 小波神经网络
%K 预测模型&lt
%K /br&gt
%K pulsewave
%K BPneuralnetwork
%K waveletneuralnetwork
%K predictionmodel
%U http://zrxuebao.hebut.edu.cn//oa/darticle.aspx?type=view&id=201803003