脉搏波信号的提取与双域分析
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摘要
通过深度解析压力式和光电式传感器检测脉搏波的原理,探索压力式传感器最佳的取脉压,最终实现对所测得的脉搏在时域和频域内分解。对20位志愿者进行脉搏波测量,利用压电薄膜传感器测量最佳取脉压并与身体质量指数(BMI)进行三阶多项式拟合标定,在时域和频域对脉搏波分别进行小波和傅里叶变换。测试结果表明,光电式脉搏波存在淹没特征点的弊端,压力式传感器能清楚地观察到特征点,但由于取脉压的问题导致波形有同体差异性。通过对个体施加最佳取脉压,压力式传感器能以高信噪比来检测脉搏波。时频双域分析可有效提取波形特征点,为连续血液测量提供了良好的数据源。
Two methods for measuring pulse wave using either a pressure or an optical sensor are studied and compared. A model for determining the optimal external force for restraining the pressure sensor is proposed. Pulse waves are measured on 20 volunteers. The restraint force on the pressure sensor is slowly varied and recorded using a piezoelectric film sensor for each person until it reaches a so-called optimal acquisition pressure under which the pulse wave has the greatest amplitude. The optimal pressure is then analyzed using polynomial regression with the body-mass index( BMI). In addition, the pulse waves are decomposed in both the time and frequency domains to identify and extract the features corresponding to physiological activities. Pulse waves measured with the optical sensor method suffer from feature point loss where certain points are submerged and indistinguishable. The pressure sensor method can reveal the feature points clearly, but the waveform exhibits variations if the external force applied isn ′ t a constant. With the application of optimal acquisition force set by our model, the latter method is able to detect pulse wave with a high signal-to-noise ratio. The dual domain analysis is shown to extract features that can be later used for continuous blood pressure monitoring.
Two methods for measuring pulse wave using either a pressure or an optical sensor are studied and compared. A model for determining the optimal external force for restraining the pressure sensor is proposed. Pulse waves are measured on 20 volunteers. The restraint force on the pressure sensor is slowly varied and recorded using a piezoelectric film sensor for each person until it reaches a so-called optimal acquisition pressure under which the pulse wave has the greatest amplitude. The optimal pressure is then analyzed using polynomial regression with the body-mass index( BMI). In addition, the pulse waves are decomposed in both the time and frequency domains to identify and extract the features corresponding to physiological activities. Pulse waves measured with the optical sensor method suffer from feature point loss where certain points are submerged and indistinguishable. The pressure sensor method can reveal the feature points clearly, but the waveform exhibits variations if the external force applied isn ′ t a constant. With the application of optimal acquisition force set by our model, the latter method is able to detect pulse wave with a high signal-to-noise ratio. The dual domain analysis is shown to extract features that can be later used for continuous blood pressure monitoring.
引文
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[10]Zheng Gang,Huang Qingjun,Yan Guodong,et al.Pulse waveform key point recognition by wavelet transform for central aortic blood pressure estimation[J].Journal of Information&Computational Science,2012,9(1):25-33.
[11]方清城.小波分析22个算法实现[M].北京:电子工业出版社,2017.
[12]刘明君,董增寿.基于改进小波变换的手臂肌电信号去噪算法的研究[J].电子技术应用,2018,44(3):122-125.
[2]LAX H,FEINBERG A W,BURTON M.Studies of the arterial pulse wave:the normal pulse wave and its modification in the presence of human arteriosclerosis[J].Journal of Chronic Diseases,1956,3(6):618-631.
[3]赵泽龙,康明铭,熊浩,等.脉搏波信号的频谱分析[J].中国科技信息,2016,41(12):103-104.
[4]吕海姣,严壮志,陆维嘉,等.一种基于脉搏波的无创连续血压测量方法[J].中国医疗杂志,2011,35(3);169-173.
[5]徐可欣,王继寸,余辉,等.脉搏波时域特征与血压相关性的研究[J].中国医疗设备,2009,24(8):42-45.
[6]宋知用.MATLAB数字信号处理[M].北京:北京航空航天大学出版社,2016.
[7]Xing Xianman,Sun Mingshan.Optical blood pressure estimation with photoplethysmography and FFT-based neural networks[J].Biomedical Optics EXPRESSS,2016,7(8):3007-3020.
[8]郭友达.可穿戴式智能血压检测系统的设计与实现[D].南京:南京理工大学,2017.
[9]李晶.不同取脉压下的脉搏波分析研究[D].河北:燕山大学,2015.
[10]Zheng Gang,Huang Qingjun,Yan Guodong,et al.Pulse waveform key point recognition by wavelet transform for central aortic blood pressure estimation[J].Journal of Information&Computational Science,2012,9(1):25-33.
[11]方清城.小波分析22个算法实现[M].北京:电子工业出版社,2017.
[12]刘明君,董增寿.基于改进小波变换的手臂肌电信号去噪算法的研究[J].电子技术应用,2018,44(3):122-125.