脉搏波蕴含有丰富的心血管功能信息,可用于无袖带血压检测。但光电容积脉搏波(PPG)信号易受噪声干扰,而血压检测的准确性依赖于高质量的PPG信号特征。由此,提出了一种集成现代信号处理技术与脉搏波特征参数分析的方法提高基于脉搏波的...脉搏波蕴含有丰富的心血管功能信息,可用于无袖带血压检测。但光电容积脉搏波(PPG)信号易受噪声干扰,而血压检测的准确性依赖于高质量的PPG信号特征。由此,提出了一种集成现代信号处理技术与脉搏波特征参数分析的方法提高基于脉搏波的无袖带血压检测的精确性。首先,联合使用集合经验模态分解与信号质量检测算法抑制噪声干扰,重构有效PPG信号,从而保证PPG信号的波形和频率特征的有效性。采用脉搏波特征参数与个体参数,建立BP神经网络血压检测模型,并通过平均影响法进行特征选取,减少冗余特征,最后利用遗传算法对神经网络进行优化,得到最优的血压估计模型。实验结果显示,所提出的血压检测方法获得的收缩压和舒张压预测误差≤10 mm Hg的百分比分别为93.1%和94.83%,其预测结果满足血压测量标准,可以有效实现无袖带血压检测。展开更多
Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to th...Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.展开更多
文摘脉搏波蕴含有丰富的心血管功能信息,可用于无袖带血压检测。但光电容积脉搏波(PPG)信号易受噪声干扰,而血压检测的准确性依赖于高质量的PPG信号特征。由此,提出了一种集成现代信号处理技术与脉搏波特征参数分析的方法提高基于脉搏波的无袖带血压检测的精确性。首先,联合使用集合经验模态分解与信号质量检测算法抑制噪声干扰,重构有效PPG信号,从而保证PPG信号的波形和频率特征的有效性。采用脉搏波特征参数与个体参数,建立BP神经网络血压检测模型,并通过平均影响法进行特征选取,减少冗余特征,最后利用遗传算法对神经网络进行优化,得到最优的血压估计模型。实验结果显示,所提出的血压检测方法获得的收缩压和舒张压预测误差≤10 mm Hg的百分比分别为93.1%和94.83%,其预测结果满足血压测量标准,可以有效实现无袖带血压检测。
基金Projects(51678071,51278071)supported by the National Natural Science Foundation of ChinaProjects(14KC06,CX2015BS02)supported by Changsha University of Science&Technology,China
文摘Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.
