基于压电陶瓷的生理信号监测算法

Physiological signal monitoring algorithm based on piezoceramic

为实现低成本、非接触式、实时监测人体呼吸、心率等有利于疾病预防和健康管理的生理信号,设计了基于压电陶瓷的非接触式生理参数监测系统。通过压电陶瓷传感器将人体呼吸、心跳以及其他微弱震动产生的压力变化信息转换为电信号,从而获得原始生理信号。采用小波硬阈值算法对原始生理信号进行预处理去噪,选择合适的小波基、分解层数及硬阈值函数,滤除干扰信号。采用变分模态分解算法对预处理后的生理信号进行分解,根据分解后各个模态分量的频谱分布特点进行生理信号的重构,分解出原始生理信号中的呼吸信号和心冲击信号。与先经过巴特沃斯滤波器预处理再进行信号分解相比,变分模态分解算法具有更好的还原性。实验结果表明,该方法既能准确分离呼吸信号、心冲击信号,也能保留心冲击信号更多的尖峰特征。

In order to achieve low-cost and non-contact health monitoring,real-time monitoring of human breathing,heart rate and other physiological signals that are beneficial to disease prevention and health management without interference,a non-contact physiological parameter monitoring system based on piezoelectric ceramics is designed.The Piezoelectric ceramic sensor converts the pressure changes caused by human breathing,heartbeat and other weak vibrations into electrical signals,thereby obtaining the original physiological signals.The wavelet hard threshold algorithm is used to preprocess the original physiological signal,and the interference signal is filtered out by selecting the appropriate wavelet base,the number of decomposition layers and the hard threshold function.VMD algorithm is used to decompose the preprocessed physiological signal.According to the spectral distribution characteristics of each modal component after decomposition,the physiological signal is reconstructed to decompose the respiratory and BCG signal in the original physiological signal.Compared with the decomposing after the Butterworth filter is preprocessed,it has better performance.The experimental results show that the method can accurately separate respiratory signal and BCG signal,and can retain more spike BCG signal characteristics.