Yang Dan, Xu Bin, Ye Linlin, Jin Jingjing
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2014 Dec;31(6):1368-72.
Ballistocardiogram (BCG) signal is a physiological signal, reflecting heart mechanical status. It can be measured without any electrodes touching subject's body surface and can realize physiological monitoring ubiquitously. However, BCG signal is so weak that it would often be interferred by superimposed noises. For measuring BCG signal effectively, we proposed an approach using joint time-frequency distribution and empirical mode decomposition (EMD) for BCG signal denoising. We set up an adaptive optimal kernel for BCG signal and extracted BCG signals components using it. Then we de-noised the BCG signal by combing empirical mode decomposition with it. Simulation results showed that the proposed method overcome the shortcomings of empirical mode decomposition for the signals with identical frequency content at different times, realized the filtering for BCG signal and also reconstructed the characteristics of BCG.
心冲击图（BCG）信号是一种生理信号，反映心脏的机械状态。无需任何电极接触受试者体表即可测量该信号，并且能够实现无处不在的生理监测。然而，BCG信号非常微弱，经常会受到叠加噪声的干扰。为了有效测量BCG信号，我们提出了一种使用联合时频分布和经验模态分解（EMD）对BCG信号进行去噪的方法。我们为BCG信号设置了一个自适应最优核，并使用它提取BCG信号分量。然后，我们将经验模态分解与之相结合对BCG信号进行去噪。仿真结果表明，该方法克服了经验模态分解对不同时刻具有相同频率成分信号的缺点，实现了对BCG信号的滤波，同时也重构了BCG的特征。
