基于CEEMDAN-PE的心冲击信号降噪方法研究

心冲击信号(BCG)是反应心脏力学特征的生理信号,能实现无电极束缚条件下的连续采集测量,然而BCG信号微弱且极易受到干扰,测量时经常会淹没在噪声中。为了有效识别BCG信号,提出一种基于自适应噪声的完全集合经验模态分解(CEEMDAN)联合排列熵(PE)的BCG降噪方法。首先,将采集到的BCG信号通过CEEMDAN分解得到一系列按频率由高到低的固有模态函数(IMF)。其次,通过PE计算各个IMF分量的值并确定有效信号的阈值范围,从而滤除信号中的高频噪声和基线漂移。最后实验结果显示,降噪后信号的幅频特性得到明显改善且信噪比较传统方法有明显提高,证明了本文降噪方法效果显著,能够有效还原BCG信号特征。

Research on BCG signal de-noising method based on CEEMDAN and PE

Ballistocardiogram (BCG) signal is a physiological signal that reflects the mechanical characteristics of the heart it can achieve continuous acquisition measurements without electrode binding. However, the BCG signal is weak and highly susceptible to interference, and is often submerged in noise during measurements. In order to effectively identify BCG signals, this paper proposes a BCG de noising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with permutation entropy (PE). Firstly, the collected BCG signal is decomposed with CEEMDAN to obtain a series of intrinsic mode function (IMF) from high to low frequencies. Secondly, the value of each IMF component is calculated with PE and the threshold range of the useful signal is determined, thereby the high frequency noise and baseline drift in the signal are filtered out. The experiment results show that the amplitude frequency characteristics of the signal after noise reduction are significantly improved, and compared with the traditional method the signal to noise ratio is significantly improved, which proves that the proposed noise reduction method has obvious effect and can effectively restore the BCG signal characteristics.