Noise Cancellation from Vibrocardiographic Signals Based on the Ensemble Empirical Mode Decomposition

Abstract

Vibrocardiographic (VCG) signals are the cardiac vibration measured at the chest surface. These signals can contain useful information for diagnosing cardiac conditions but are often contaminated by noise. Although band-pass and adaptive filters were used for noise removal from similar signals, the utility of ensemble empirical mode decomposition (EEMD) for filtering VCG was not previously investigated. In this study, an EEMD-based filter was proposed and tested. The filtering scheme first decomposed the VCG waveform into a set of intrinsic mode functions (IMF) then utilized the partial sum of IMFs to remove white noise that was added to simulated VCG signals. To measure the filter effectiveness, the normalized root-mean-square error (NRMSE) between the clean (i.e., before adding noise) and filtered signals was calculated for signal-to-noise ratios ranging from 1 to 20 dB. The EEMD-based filter performance was also compared with traditional methods such as Wiener filter. This comparison suggested that EEMDbased filter outperformed the Wiener filter in noise removal from simulated VCG. These results also suggested that EEMD may be utilized for white noise removal from actual VCG signals. Further investigations are warranted to study the relation between IMFs and different types of noise, which can enhance the effectiveness of EEMD-based filters in removing these noise types from actual VCG signals.

Analysis of seismocardiographic signals using polynomial chirplet transform and smoothed pseudo Wigner-Ville distribution

“…Therefore, a computer assisted analysis may help find possible correlations between the SCG signal characteristics and cardiovascular conditions. Many methods have been used to investigate the SCG features [13]- [15] including time-frequency distributions (TFD) [16]. The current study aims at comparing the performance of some available TFD methods for estimating the spectral content of SCG signals.…”

Heart Rate Monitoring During Different Lung Volume Phases Using Seismocardiography

“…The sensor was affixed to the left sternal border at the level of the 4 th intercostal space using a doublesided medical-grade tape. This location was chosen due to its high signal-to-noise ratio [15], [16]. The accelerometer's z-axis was aligned perpendicular to the subject chest surface, while the x-and y-axes were aligned parallel to the axial and mediolateral directions, respectively.…”

Filtering Techniques for Chaotic Signal Processing

“…Numerous filtration techniques may be applied to denoise communication signals: recursive and non-recursive filtering [23], median filtering [23], algorithms based on discrete wavelet transform [24], Wiener filters [25], and empirical mode decomposition (EMD) [26,27]. The filter is placed at the first stage of the receiver, as presented in Figure 7.…”

“…Thus, EMD makes it possible to decompose a signal into non-stationary and nonlinear intrinsic mode functions (IMF). Discarding A description of abovementioned methods can be found in referenced publications [23][24][25][26][27]. In this paper, we focus on two filtration techniques: EMD, as a relatively little known technique whose usage in chaotic communications has never been reported; and a novel approach involving chain of driven oscillators.…”