Research On Removal Of Baseline Wander In ECG Signals

ECG signals can be used to detect and diagnose heart disease.In practice,the ECG signals are often corrupted by baseline wanders that are mainly caused by respiratory activity,body movements,skin-electrode interface,varying impedance between electrodes and skin due to poor electrode contact and perspiration.The presence of baseline wanders can degrade the ECG signal quality and may severely affect the PQRST morphologies.Thus,removal of BW has become an crucial first step in most ECG signal processing applications including cardiac arrhythmias recognition,heart rate variability analysis,continuous blood pressure measurement and so on.ECG signal is a kind of non-stationary and non-linear signal.The traditional methods of removing the baseline wander in the ECG signal are often due to excessive or incomplete denoising when the ECG signal is denoised,which easily results in the loss of a large amount of nonlinear characteristic information.This will destroy the dynamic characteristics of the ECG signal itself,which will adversely affect the subsequent analysis of ECG information.In view of the non-stationary and non-linear characteristics of ECG signal,this paper discusses the application of two kinds of signal decomposition algorithms in the baseline wander removal of ECG signals,namely the variational mode decomposition algorithm and the singular spectrum analysis algorithm.Variational Mode Decomposition was proposed by Konstantin Dragomiretskiy in 2014.Variational mode decomposition is a new,entirely non-recursive signal decomposition method,it can decompose the given signal into a set of modes which around the center frequencies.The variational mode decomposition can be used to decompose the ECG signal into several modes.Then removing the mode corresponding to the baseline wander and reconstructing the remaining modes can obtain the ECG signal after the baseline wander is removed.The Singular Spetrum Analysis method was first proposed by Colebrook in 1978.Singular spectrum analysis is a powerful method for studying nonlinear time series data.It can extract the different components of the original time series.With SSA applied,the ECG signal can be decomposed into trends,oscillations or noise components based on the singular value decomposition.Only the first eigenvalue component that may be interpretable as basic trend is selected to reconstruct the BW signal and then removal it from the ECG signal.In this paper,we use MATLAB as a simulation tool,use the ECG signals as simulation signals which are provided by arrhythmia database of Massachusetts Institute of Technology.We used the advantages of variational mode decomposition and singular spectrum analysis in processing non-stationary signals,and studied how to remove the baseline wander in ECG signals.The experimental results show that compared with the existing baseline wander removal algorithm,the two denoising algorithms proposed in this paper not only are more adaptive but also perform better in terms of correlation coefficient and signal-to-noise ratio.