Wavelet-Based Filtering Method for Sleep EEG Signal

2011 ◽  
Vol 130-134 ◽  
pp. 2160-2165
Author(s):  
Hua Qiang ◽  
Rui Yang ◽  
Guo Dong Zhang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Decomposition ◽  
Sleep Eeg ◽  
Signal Interference ◽  
Reconstruction Method ◽  
Threshold Method ◽  
Baseline Drift ◽  
Modulus Maximum ◽  
Common Signal ◽  
Wavelet Modulus Maximum

In this paper, in accordance with several common signal interference in sleep EEG detection, it is processed by wavelet transform. It mainly includes: ①.remove white noise from EEG using wavelet threshold method; ②.remove baseline drift from EEG using wavelet decomposition and reconstruction method; ③.remove sharp pulse interference using wavelet modulus maximum algorithm; ④.remove EMG from EEG using wavelet decomposition and reconstruction as well as modulus maximum method. The results of simulation study show that: it can filter a variety of common interference in EEG detection preferably by wavelet transform.

An Approach Based on Wavelet Transform to Remove the Noises of ECG

2012 ◽  
Vol 562-564 ◽  
pp. 1899-1902
Author(s):  
Guo Zhuang Liang ◽  
Su Fang Sun ◽  
Jing Xia Wei
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Medical Diagnosis ◽  
Wavelet Decomposition ◽  
Power Line ◽  
Reconstruction Method ◽  
Threshold Method ◽  
Baseline Drift ◽  
Power Line Interference ◽  
Line Interference

In the acquisition process of ECG, noise, which mainly consists of power line interference baseline drift and the EMG interference, often exists due to the instrument, the human body and other aspects. This noise mixed with the ECG, will causes ECG distortion, which makes the whole ECG waveform blurred, and impacts the subsequent signal processing and analysis. In this paper, Coif4 wavelet is used to make the ECG decomposed by 8 scale; at the same time, the wavelet decomposition and reconstruction method is used to remove baseline drift, and then the improved wavelet threshold method is used to remove power line interference and the EMG interference waveform to obtain accurate geocentric, providing a more accurate basis for the medical diagnosis.

Study on White Noise Suppression Using Complex Wavelet Threshold Algorithm

2014 ◽  
Vol 521 ◽  
pp. 347-351 ◽  
Author(s):  
Shu Qi Zhang ◽  
Jin Zhong Li ◽  
Rui Guo ◽  
Hao Tang ◽  
Tao Zhao ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
White Noise ◽  
Noise Suppression ◽  
Threshold Selection ◽  
Complex Wavelet Transform ◽  
Threshold Method ◽  
Modulus Maximum ◽  
Complex Wavelet ◽  
Wavelet Transform Modulus Maximum ◽  
Selection Of

The complex wavelet transform modulus maximum of the PD signal increases with scale, while the complex wavelet transform modulus maximum of white noise decreases with scale. According to the characteristics, a study on white noise suppression using the effective complex wavelet coefficient (ECWC) threshold method is launched in this paper and a comparison is conducted with the wavelet threshold denoising method of threshold selection of Stein unbiased risk estimate theory and threshold selection of minimax theory. The PD signal denoising results show that ECWC threshold method is more effective and the distortion of the extract PD signal is lower compared with the other method.

scholarly journals Extraction method of weak underwater acoustic signal based on the combination of wavelet transform and empirical mode decomposition

2021 ◽  
Vol 12 ◽  
pp. 7
Author(s):  
Junbing Shi ◽  
Yingmin Wang ◽  
Xiaoyong Zhang ◽  
Libo Yang
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Intrinsic Mode Functions ◽  
Threshold Method ◽  
Underwater Acoustic ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Combined Algorithm

When studying underwater acoustic exploration, tracking and positioning, the target signals collected by hydrophones are often submerged in strong intermittent noise and environmental noise. In this paper, an algorithm that combines empirical mode decomposition and wavelet transform is proposed to achieve the efficient extraction of target signals in the environment with strong noise. First the calibration of baseline drift is performed on the algorithm, and then it is decomposed into different intrinsic mode functions via empirical mode. The wavelet threshold processing is conducted according to the correlation coefficient of each mode component and the original signal, and finally the signals are reconstructed. The simulation and experiment results show that compared with the conventional empirical mode decomposition method and wavelet threshold method, when the signal-to-noise ratio is low and there exist high-frequency intermittent jamming and baseline drift, the combined algorithm can better extract the target signal, laying the foundation for direction-of-arrival estimation and target positioning in the next step.

scholarly journals ECG Signal Denoising by Discrete Wavelet Transform

2017 ◽  
Vol 13 (09) ◽  
pp. 51 ◽  
Author(s):  
Mounaim Aqil ◽  
Atman Jbari ◽  
Abdennasser Bourouhou
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Threshold Method ◽  
Baseline Drift ◽  
Ecg Denoising ◽  
The Mean ◽  
Power Line Interference ◽  
Electrocardiogram Ecg ◽  
Selection Of

<p>The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG denoising are based on discrete wavelet transform (DWT). In the other side the performances of denoising process considerably influence the operations that follow. These performances are quantified by some ratios such as the output signal on noise (SNR) and the mean square error (MSE) ratio. This is why the optimal selection of denoising parameters is strongly recommended. The aim of this work is to define the optimal wavelet function to use in DWT decomposition for a specific case of ECG denoising. The choice of the appropriate threshold method giving the best performances is also presented in this work. Finally the criterion of selection of levels in which the DWT decomposition must be performed is carried on this paper. This study is applied on the electromyography (EMG), baseline drift and power line interference (PLI) noises.</p>

Creation of codes based on wavelet transformation and its application in ADV612 chips

2017 ◽  
Vol 15 (02) ◽  
pp. 1750014 ◽  
Author(s):  
Alla Levina ◽  
Sergey Taranov
Keyword(s):  
Wavelet Transform ◽  
Video Processing ◽  
Wavelet Decomposition ◽  
Filter Bank ◽  
Mathematical Concepts ◽  
Coding Scheme ◽  
Image And Video Processing ◽  
Error Coding ◽  
New Construction ◽  
Robust Codes

Theory of wavelet transform is a powerful tool for image and video processing. Mathematical concepts of wavelet transform and filter bank have been studied carefully in many works. This work presents application of new construction of linear and robust codes based on wavelet decomposition and its application in ADV612 chips. We present the model of the error-coding scheme that allows to detect errors in the ADV612 chips with high probability. In our work, we will show that developed and presented scheme of protection drastically improves the resistance of ADV612 chips to malfunctions and errors.

Exterior Ballistic Data Processing by SVD and Wavelet Transform

2012 ◽  
Vol 562-564 ◽  
pp. 1394-1397
Author(s):  
Yu Hua Dong ◽  
Hai Chun Ning
Keyword(s):  
Wavelet Transform ◽  
Phase Space ◽  
Data Processing ◽  
Wavelet Decomposition ◽  
Hankel Matrix ◽  
Singular Value ◽  
Experimental Data Processing ◽  
Abnormal Detection ◽  
Reconstructed Phase Space ◽  
Trajectory Measurement

This paper proposes a method of wavelet transform combined with SVD (Singular Value Extracting), and the abnormal data elimination in its trajectory measurement is studied. After the wavelet decomposition of the observed data, combining the approximate component and the detail component, the phase space is reconstructed. The increment criterion of singular entropy is used for the input observed matrix of SVD, and the singular value is selected. Then the original signal is reconstructed by SVD inverse transform. This method overcomes the distortion problem of data end in phase space reconstruction by Hankel matrix. The reconstructed phase space by components of wavelet decomposition is orthogonal. So it further improves the accuracy of noise reduction and abnormal detection by SVD. The results of experimental data processing show the effectiveness of this method proposed in the paper.

Research on Denoising of GPS Data Based on Nonlinear Wavelet Transform Threshold Method

2012 ◽  
Vol 446-449 ◽  
pp. 926-936
Author(s):  
De Bao Yuan ◽  
Xi Min Cui ◽  
Guo Wang ◽  
Jing Jing Jin ◽  
Wan Yang Xu
Keyword(s):  
Wavelet Transform ◽  
Gps Data ◽  
Threshold Method

scholarly journals An Effective Method to Denoiseeeg, ECG and PPG Signals based on Meyer Wavelet Transform

2018 ◽  
Vol 7 (3.34) ◽  
pp. 678
Author(s):  
P Thamarai ◽  
Dr K.Adalarasu
Keyword(s):  
Wavelet Transform ◽  
Adaptive Filter ◽  
Wavelet Decomposition ◽  
Motion Artifact ◽  
Signal Quality ◽  
Frequency Noise ◽  
Meyer Wavelet ◽  
Discrete Transform ◽  
Reference Input

In this analysis, the prevailing role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the constant and the discrete transform are considered in turn.A Wavelet denoising is functional on the original signal to eradicate high frequency noise, and then a process based on Meyer wavelet transform combined with adaptive filter is functional to eradicate the motion artifact. This approach uses Meyer Wavelet decomposition to extract the motion artifact, which is subsequently utilized as the reference input of an adaptive filter for noise cancellation. The technique diminishes the overhead of the circuit because it does not need a separate collection of reference input signal which link to noise. Testing results illustrate that this approach can efficiently remove motion artifact and make better the signal quality. 

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