基于EMD-ICA的心冲击信号降噪研究
首发时间:2017-07-27
摘要:心冲击(BCG)信号是反映心脏机械运动的生理信号,能实现无束缚采集测量。但BCG信号微弱,易受干扰,测量时经常会淹没在噪声中。为了有效识别BCG信号,本文提出一种基于经验模态分解联合独立成分分析的BCG信号降噪方法。首先,将含噪BCG信号进行EMD分解,获得一系列按频率从高到低的固有模态分量(IMF),采用模态相关准则进行信号层与噪声层的判定;其次,将分界之上的IMF分量构建虚拟噪声通道,基于ICA算法对原始BCG信号进行盲源分离,从而得到降噪后的BCG信号;最后,利用快速傅里叶变换(FFT)计算功率谱密度(DSP),得到BCG信号降噪前后的信号特征。对比EMD降噪和小波降噪方法,计算各个方法降噪后与原信号的相关系数。结果表明本文方法降噪效果明显,且能有效还原BCG信号特征,说明本文方法的有效性。
关键词: 心冲击信号 经验模态分解 独立成分分析 模态相关 降噪
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BCG signal de-noising method research based on EMD and ICA
Abstract:Ballistocardiogram (BCG) signal is a physiological signal, reflecting heart mechanical status. It can achieve non-binding acquisition measurement. However, BCG signal is so weak that it would often be interfered by superimposed noises. For measuring BCG signal effectively, this paper proposed a de-noising method of BCG signal based on empirical mode decomposition (EMD) and independent component analysis (ICA). Firstly, the noisy BCG signal is decomposed by EMD to obtain a series of intrinsic mode components (IMF) ranked by frequency in descending order, and the EMD mode was used to distinguish the boundary of noise and useful signal and remove the maximum noise. Secondly, the IMF components of above the boundary to construct virtual noise channel, and the blind source separated of the original BCG signal based on ICA algorithm to extract the de-noising BCG signal. Finally, the power spectral density (DSP) is calculated by using the fast Fourier transform (FFT) to obtain the signal characteristics before and after BCG signal de-noising. Compare the END and wavelet de-noising methods to calculate the mutual-correlation coefficient between the noise reduction and the original signal. The results show that this method is effective and also reconstructed the characteristics of BCG, which shows the effectiveness of this method.
Keywords: Ballistocardiogram signal empirical mod decomposition independent component analysis model correlation de-noising
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