Efficient Artifact Elimination in Cardiac Signals using Variable Step Size Adaptive Noise Cancellers

2012 ◽  
Vol 2 (1) ◽  
pp. 35-51 ◽  
Author(s):  
S. Yasmin Fathima ◽  
G. V. S. Karthik ◽  
M. Zia Ur Rahman ◽  
A. Lay-Ekuakille
Keyword(s):  
Signal To Noise Ratio ◽  
Motion Artifacts ◽  
Variable Step Size ◽  
Ecg Signal ◽  
Large Signal ◽  
Signal To Noise ◽  
Step Size ◽  
Variable Step ◽  
Adaptive Noise ◽  
Power Line Interference

In this paper several variable step size adaptive filter structures for extracting high resolution electrocardiographic (ECG) signals are presented which estimates the deterministic components of the ECG signal and removes the artifacts. The noise canceller minimizes the mean square error (MSE) between the input noisy ECG signal and noise reference. Different noise canceller structures are proposed to remove diverse forms of artifacts: power line interference, baseline wander, muscle artifacts and electrode motion artifacts. The proposed implementation is suitable real time applications, where large signal to noise ratios with fast convergence are required. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio, convergence rate and MSE.

A new adaptive variable step size natural gradient BSS algorithm

2021 ◽  
pp. 1-12
Author(s):  
Junqing Ji ◽  
Xiaojia Kong ◽  
Yajing Zhang ◽  
Tongle Xu ◽  
Jing Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Coefficient ◽  
Gradient Algorithm ◽  
Variable Step Size ◽  
Natural Gradient ◽  
Threshold Method ◽  
Step Size ◽  
Morphological Component Analysis ◽  
Variable Step ◽  
Separation Degree

The traditional blind source separation (BSS) algorithm is mainly used to deal with signal separation under the noiseless model, but it does not apply to data with the low signal to noise ratio (SNR). To solve the problem, an adaptive variable step size natural gradient BSS algorithm based on an improved wavelet threshold is proposed in this paper. Firstly, an improved wavelet threshold method is used to reduce the noise of the signal. Secondly, the wavelet coefficient layer with obvious periodicity is denoised using a morphological component analysis (MCA) algorithm, and the processed wavelet coefficients are recombined to obtain the ideal model. Thirdly, the recombined signal is pre-whitened, and a new separation matrix update formula of natural gradient algorithm is constructed by defining a new separation degree estimation function. Finally, the adaptive variable step size natural gradient blind source algorithm is used to separate the noise reduction signal. The results show that the algorithm can not only adaptively adjust the step size according to different signals, but also improve the convergence speed, stability and separation accuracy.

Research of Vector Hydrophone Adaptive Signal Processing

2013 ◽  
Vol 423-426 ◽  
pp. 2496-2506
Author(s):  
Bo Le Ma ◽  
Jing Fang Cheng ◽  
Wei Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Adaptive Signal Processing ◽  
Line Spectrum ◽  
Variable Step Size ◽  
Step Size ◽  
Spectrum Detection ◽  
Vector Hydrophone ◽  
Variable Step ◽  
Simulation And Experiment ◽  
Adaptive Signal

As a useful tool for line spectrum detection in underwater signal ,ALE has been used wildly. But there are still some problems to influence the effect of ALE. This paper gives three problems on ALE and analyses these.Then by the characteristics of vector hydrophone and a improved variable step size LMS,this paper constructs a cascade double input with variable step size based on vector hydrophone line spectrum enhancer . This algorithm restrains the noise of main channel twice ,meanwhile controls the noise in reference channel , so as to improve signal to noise ratio better. At the same time ,because of adopting the improved variable step size LMS, the steady-state error is reduced. From the results of simulation and experiment, the method presented in this paper can have a better effect of line spectrum enhancement.

scholarly journals Performance of Adaptive Noise Cancellation with Normalized Last-Mean-Square Based on the Signal-to-Noise Ratio of Lung and Heart Sound Separation

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Noman Q. Al-Naggar ◽  
Mohammed H. Al-Udyni
Keyword(s):  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Mean Square ◽  
Adaptive Noise Cancellation ◽  
Signal To Noise ◽  
Step Size ◽  
Nlms Algorithm ◽  
Noise Ratio ◽  
Adaptive Noise

The adaptive algorithm satisfies the present needs on technology for diagnosis biosignals as lung sound signals (LSSs) and accurate techniques for the separation of heart sound signals (HSSs) and other background noise from LSS. This study investigates an improved adaptive noise cancellation (ANC) based on normalized last-mean-square (NLMS) algorithm. The parameters of ANC-NLMS algorithm are the filter length Lj parameter, which is determined in 2n sequence of 2, 4, 8, 16, … , 2048, and the step size (μn), which is automatically randomly identified using variable μn (VSS) optimization. Initially, the algorithm is subjected experimentally to identify the optimal μn range that works with 11 Lj values as a specific case. This case is used to study the improved performance of the proposed method based on the signal-to-noise ratio and mean square error. Moreover, the performance is evaluated four times for four μn values, each of which with all Lj to obtain the output SNRout matrix (4 × 11). The improvement level is estimated and compared with the SNRin prior to the application of the proposed algorithm and after SNRouts. The proposed method achieves high-performance ANC-NLMS algorithm by optimizing VSS when it is close to zero at determining Lj, at which the algorithm shows the capability to separate HSS from LSS. Furthermore, the SNRout of normal LSS starts to improve at Lj of 64 and Lj limit of 1024. The SNRout of abnormal LSS starts from a Lj value of 512 to more than 2048 for all determined μn. Results revealed that the SNRout of the abnormal LSS is small (negative value), whereas that in the normal LSS is large (reaches a positive value). Finally, the designed ANC-NLMS algorithm can separate HSS from LSS. This algorithm can also achieve a good performance by optimizing VSS at the determined 11 Lj values. Additionally, the steps of the proposed method and the obtained SNRout may be used to classify LSS by using a computer.

Nonlinear Noise Canceller by Neural Network with Variable Step Size

2013 ◽  
Vol 433-435 ◽  
pp. 709-712
Author(s):  
Shou Zhong Zhang
Keyword(s):  
Neural Network ◽  
Variable Step Size ◽  
Signal To Noise ◽  
Step Size ◽  
Nonlinear Correlation ◽  
Transversal Filter ◽  
Interference Noise ◽  
Variable Step ◽  
Nonlinear Noise ◽  
Simulation Results

Neural network is acted as noise canceller to implement noise cancel under the condition of interference noise has nonlinear correlation to reference noise. If interference noise has nonlinear correlation to reference noise, the transversal filter has weak effect to cancel the noise in the signal. Neural network has nonlinear characteristic transfer and can solve this problem, and a new variable step size algorithm is proposed to further improve the performance. Computer simulation results show that neural network noise canceller has better signal to noise gain for nonlinear noise.

An Improved Denoising of Electrocardiogram Signals Based on Wavelet Thresholding

2021 ◽  
Vol 51 ◽  
pp. 117-129
Author(s):  
Pinjala N. Malleswari ◽  
Ch. Hima Bindu ◽  
K. Satya Prasad
Keyword(s):  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Ecg Signal ◽  
Noise Power ◽  
Signal To Noise ◽  
Noise Elimination ◽  
Electrocardiogram Signals ◽  
Conventional Methods ◽  
Noise Ratio ◽  
Power Line Interference

Electrocardiogram (ECG) is the most important signal in the biomedical field for the diagnosis of Cardiac Arrhythmia (CA). ECG signal often interrupted with various noises due to non-stationary nature which leads to poor diagnosis. Denoising process helps the physicians for accurate decision making in treatment. In many papers various noise elimination techniques are tried to enhance the signal quality. In this paper a novel hybrid denoising technique using EMD-DWT for the removal of various noises such as Additive White Gaussian Noise (AWGN), Baseline Wander (BW) noise, Power Line Interference (PLI) noise at various concentrations are compared to the conventional methods in terms of Root Mean Square Error (RSME), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Cross-Correlation (CC) and Percent Root Square Difference (PRD). The average values of RMSE, SNR, PSNR, CC and PRD are 0.0890, 9.8821, 14.4464, 0.9872 and 10.9036 for the EMD approach, respectively, and 0.0707, 10.7181, 16.2824, 0.9874 and 10.7245 for the proposed EMD-DWT approach, respectively, by removing AWGN noise. Similarly BW noise and PLI are removed from the ECG signal by calculating the same quality metrics. The proposed methodology has lower RMSE and PRD values, higher SNR, PSNR and CC values than the conventional methods.

Multiple-input adaptive seismic noise canceller for the attenuation of nonstationary coherent noise

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. V139-V150 ◽  
Author(s):  
Charlotte Sanchis ◽  
Alfred Hanssen
Keyword(s):  
Seismic Data ◽  
Median Filter ◽  
Variable Step Size ◽  
Step Size ◽  
Coherent Noise ◽  
Time Frequency ◽  
Multiple Input ◽  
Variable Step ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller

Attenuation of coherent noise, typically weather generated noise and more specifically swell noise, is a major concern in seismic. Such noise, usually characterized by its low-frequency content and high amplitudes, is a common problem in seismic acquisition and, in particular, for marine data. We propose a multiple-input adaptive noise canceller as a solution to attenuate nonstationary coherent noise. This filter uses multiple noise sequences to estimate the noise contained in each trace. This noise estimate is then subtracted to obtain an estimate of the trace whose coherent noise has been attenuated. For the implementation of the adaptive filter, we use a variable step-size normalized least mean squares algorithm, as it is known for its simplicity and robustness. In addition, we demonstrate that variable step-size is necessary for this filter to adapt to the changing statistics of the seismic data. This method is tested on real marine seismic data and compared to a time-frequency median filter and a second-order high-pass Butterworth filter. We demonstrate that the multiple-input adaptive noise canceller is a powerful and efficient filter to attenuate swell noise while preserving the seismic reflections. Depending on the noise configuration, it can be used either by itself or in combination with a time-frequency median filter to obtain the best results.

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