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 de-noising. 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.
Calculation of cardiac hemodynamic parameters is based on accurate detection of feature points in impedance cardiogram. According to these parameters, doctors can determine heart conditions, so it is very important to accurately detect the feature point of impedance differential signals. This article presents a process in which we used wavelet threshold method to de-noise signals, and then detected the feature points after six layers wavelet decomposition by using bior3.7. The experimental data were collected from healthy persons in our laboratory and twenty two clinical patients in Chongqing Daping Hospital by using KF_ICG instrument. The results indicated that this method could precisely detect feature points whether it was from healthy people or clinical patients. This helps to achieve the application of noninvasive detection cardiac hemodynamic parameters in clinical treatments by using impedance method.
Ensemble empirical mode decomposition (EEMD) is an effective method for non-stationary signal analysis, such as electrocardiogram (ECG) signals. However, the precision and correctness of EEMD are affected by the two parameters, ratio of the added noise and ensemble number. The values of two parameters are set relying on experience and lacking of adaptability for uncertain signals. In order to solve these problems, we proposed a method based on white noise decomposed by EEMD in the present study shown in this paper. Empirical mode decomposition (EMD) was applied to decompose the signal to different intrinsic mode functions (IMFs) in the de-noising process. The white noise IMFs were selected to constitute high frequency part based on the character that the product of the energy density of white noise and its average period tended to be a constant. Then the two parameters of EEMD were adaptively obtained according to the criterion which was used to avoid modal aliasing. Experimental results showed that the method was an effective one for ECG signal de-noising.