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心冲击(BCG)信号是反映心脏机械运动的生理信号,测量中无需在受试者身体表面贴附电极,能实现无感觉生理监护。但BCG信号微弱,易受到干扰,测量时经常被淹没在噪声中。为了有效识别BCG信号,提出一种基于时频联合分布和经验模态分解(EMD)的BCG信号降噪方法。该方法先建立BCG信号的自适应最优核,然后在时频平面内提取BCG信号分量,最后根据EMD原理对BCG信号分量进行滤波,从而实现BCG信号降噪。仿真研究表明,该方法克服了EMD处理在不同时间含有相同或相似频率成分信号时的不足,所提出方法实现了BCG信号降噪,可以有效还原BCG信号特征。
Cardiac impact (BCG) signals are physiological signals that reflect the mechanical movement of the heart. There is no need to attach electrodes to the body surface of the subject for measurement without sensory physiology. However, BCG signals are weak and susceptible to interference and are often submerged in noise during measurements. In order to effectively identify BCG signals, a BCG signal denoising method based on time-frequency joint distribution and empirical mode decomposition (EMD) is proposed. The method first builds the BCG signal adaptive optimal kernel, and then extracts the BCG signal components in the time-frequency plane. Finally, the BCG signal components are filtered according to the EMD principle to achieve the BCG signal noise reduction. The simulation results show that this method overcomes the shortcomings of the EMD signal processing in different time with the same or similar frequency components. The proposed method realizes the BCG signal noise reduction, which can effectively restore the BCG signal characteristics.