Power Line Interference Noise Research Articles

Basis pursuit sparse decomposition using tunable-Q wavelet transform (BPSD-TQWT) for denoising of electrocardiograms.

The electrocardiogram (ECG) is an essential diagnostic tool to identify cardiac abnormalities. So, the primary issue in an ECG acquisition unit is noise interference. Essentially, the prominent ECG noise sources are power line interference (PLI) and Baseline drift (BD). Therefore, in the study, a new technique called the basis pursuit sparse decomposition (BPSD) using tunable-Q wavelet transform (TQWT) is proposed to remove the PLI and BD present in the ECG recordings. Chiefly, the TQWT method is a wavelet transform with distinct Quality factors (Q) which can adjust the signal to the natural non-stationary behaviour in time and space. Further, the method decomposes the signal into high-Quality factor and low-Quality factor components of wavelet coefficients to eliminate PLI and BD by choosing appropriate redundancy (r) and decomposition levels (J2). The 'r' and 'J' values are chosen based on the trial-and-error method concerning signal-to-noise ratio (SNR). It has been found that the PLI noise has been suppressed significantly with the redundancy of 3 and decomposition levels of 10; more so, the BD has been removed with the redundancy of 4 and decomposition levels of 19. The proposed method BPSD-TQWT was evaluated using the open-source MIT-BIH Arrhythmia database and the real-time ECG recordings collected through a wearable Silver Plated Nylon Woven (Ag-NyW) textile-based ECG monitoring system. The performance was then evaluated using fidelity metrics such as SNR, maximum absolute error (MAX), and normalized cross-correlation coefficient (NCC). The results were compared with IIR filter, stationary wavelet transform (SWT), non-local means (NLM) and local means (LM) methods. Using the proposed method on MIT-BIH Arrhythmia Database, performance evaluation parameters such as SNR, MAX, and NCC were improved by 4.3dB and 6.8dB, 0.37 and 0.78, 0.2 and 0.46 compared to IIR and SWT methods respectively. On the other hand, using the proposed method on the real-time datasets, values of SNR, MAX, and NCC were improved by 0.3dB and 0.6dB, 0.009 and 0.74 and 0.3 and 0.35 compared to IIR and SWT methods respectively. Finally, it can be concluded that the proposed method shows improved performance over IIR, SWT, NLM and LM methods for PLI and BD removal.

FPGA-based reservoir computing system for ECG denoising

ECG is corrupted by various noises such as baseline wander, channel noise, Electromyogram (EMG) noise, and power line interference (PLI). These noises make diagnosis difficult. In this paper, we implement an inexpensive, portable device that can remove noise from the corrupted ECG signals with high performance, speed, and privacy. First, we apply a Single Node Reservoir Computing (SNRC) architecture to clean the corrupted ECG signal with high performance. Second, we implement our technique on a portable inexpensive FPGA device that enables us to achieve high speed and privacy. In this manuscript, we simulate two noises: the typical EMG and PLI noises. To evaluate our technique, we use three performance metrics, namely, the output SNR improvement (SNRimp), the mean square error (MSE), and the percentage root mean square difference (PRD). The data is collected from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database. With input SNR = 0 dB, the proposed system achieves SNRimp of 15.8 and PRD of 24.6, in the case of EMG noise and SNRimp of 25.7and PRD of 4.9, in the case of PLI noise. Comparison results to the related works show the advantages of the proposed system.

Performance Evaluation of Wavelet De-Noising Schemes for Suppression of Power Line Noise in Electrocardiogram Signals

Power line noise introduces distortions to recorded electrocardiogram (ECG) signals. These distortions compromise the integrity and negatively affect the interpretation of the ECG signals. Despite the fact that the amplifiers used in biomedical signal processing have high common mode rejection ratio (CMRR), ECG recordings are still often corrupted with residual Power Line Interference (PLI) noise. Further improvement in the hardware solutions do not have significant achievements in PLI noise suppression but rather introduce other adverse effects. Software approach is necessary to refine ECG data. Evaluation of PLI noise suppression in ECG signal in the wavelet domain is presented. The performance of the Hard Threshold Shrinkage Function (HTSF), the Soft Threshold Shrinkage Function (STSF), the Hyperbola Threshold Shrinkage Function (HYTSF), the Garrote Threshold Shrinkage Function (GTSF), and the Modified Garrote Threshold Shrinkage Function (MGTSF) for the suppression of PLI noise are evaluated and compared with the aid of an algorithm. The optimum tuning constant for the Modified Garrote Threshold Shrinkage Function (MGTSF) is found to be 1.18 for PLI noise. GTSF is found to have best performance closely followed by MGTSF in term of filtering Gain. HTSF recorded the lowest Gain. Filtering against PLI noise in the wavelet domain preserves the key features of the signal such as the QRS complex.

Representation and Denoising of ECG Signal Using Hybrid Filtering Approach

Electrocardiogram (ECG) signal plays an imperative role in monitoring and examining the health condition of the heart. ECG signal represents the electrical activity of the heat. The most consequential noises that degrade important features in ECG signal are powerline interference noise, external electromagnetic field interference noise, baseline wandering and electroencephalogram noise. The features of ECG signal obtained in time domain is not sufficient for analyzing the ECG signal. As the signal is non-stationary, the time-frequency representation can be used for feature extraction. The Short Time Fourier Transform can be used but its time frequency precision is not optimal. In this current project, we will be able to implement the ideology proposed to overcome the problem among various time frequency transformation. The discrete wavelet transform (DWT) is used which gives effective results for non-stationary signals like ECG signal which may be often contaminated. The combination of Savitzky-Golay filtering and DWT can be used for ECG denoising and feature extraction which has the advantage of preserving the important feature by elimination the noise components. The method is applied for the database which is taken from MIT- BIH arrhythmia and the algorithm is implemented in MATLAB platform.

Noise Cancellation in Electrocardiography Signal using Kalman LMS Algorithm in DSP Processor

Heart health becomes more important to monitor, which is analyzed using Electrocardiography (ECG) signal. ECG signal can be corrupted by different forms of noises, when it is acquired. These noises mislead the ECG analysis and reduce the chance of accurate identification of the ECG signal features. To remove these noises, various adaptive and non adaptive filter techniques are used. This paper proposes a methodology to remove power line interference noise, using various adaptive techniques, like least mean square (LMS) algorithm, normalized least mean square (NLMS) algorithm and Kalman least mean square (KLMS) algorithm. All three filters results are compared based on evaluation metrics like signal to noise ratio (SNR), Root mean square error and convergence. KLMS algorithm is analyzed, as it overcomes the drawback of LMS and NLMS algorithm. And the advantage of KLMS algorithm is that; it is step size independent, and has least root mean square error (RMSE) and converges quickly. The results have been concluded with the MIT-BIH arrhythmia data base and simulated using MATLAB software. The algorithms are implemented in TMS320C6713 DSP kit using Code composer studio v3. TMS320C6713 is use Very long instruction word (VLIW), which helps to compute numerically complex algorithms.

Base Line Wander, Breathing, Power Line Interference Noise Suppression and QRS Detection in Scilab

Base Line Wander, Breathing, Power Line Interference Noise Suppression and QRS Detection in Scilab

A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals.

Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.

ECG signal denoising by fractional wavelet transform thresholding

The analysis of electrocardiogram (ECG) signals allows experts to diagnose several cardiac disorders. However, the accuracy of such diagnosis depends heavily on the signal quality. In this paper, an efficient method based on fractional wavelet decomposition coupled with thresholding techniques is proposed for noise removal. The usual low-pass and high-pass filters of the wavelet transform are replaced by fractional-order ones. Thus, fractional wavelets are proposed, simulated, and compared to other wavelets for ECG denoising. The denoising process was made operational by the means of an appropriate choice of the wavelet transform coefficient thresholding and the wavelet decomposition level of the signal. Considering the relative error metrics, the best wavelet function for efficient denoising is the fractional one. In our study, we have used eight real ECG signals from the Physionet MITBIH. In order to prove the effectiveness of our method, we investigated the filtering of two types of noises, namely Gaussian white noise and power-line interference (PLI) noise. The proposed method removed the Gaussian white noise completely and had better performance on the PLI noise. Considering classical metrics of assessment, results show the advantage of the proposed method compared to other types of wavelets. The proposed method is the most suitable one for removing PLI and Gaussian white noise from ECG signals with superior performance than other wavelets. Also, it can be applied for high-frequency denoising even without a priori frequency knowledge.

EMG Signal Filtering Based on Variational Mode Decomposition and Sub-Band Thresholding.

Surface electromyography (EMG) signals are inevitably contaminated by various noise components, including powerline interference (PLI), baseline wandering (BW), and white Gaussian noise (WGN). These noises directly degrade the efficiency of EMG processing and affect the accuracy and robustness of further applications. Currently, most of the EMG filters only target one category of noise. Here, we propose a novel filter to remove all three types of noise. The noisy EMG signal is first decomposed into an ensemble of band-limited modes using variational mode decomposition (VMD). Each category of noise is located within specific modes and is separately removed in sub-bands. In particular, WGN is suppressed by soft thresholding with a noise level-dependent threshold. The denoising performance was assessed from simulated and experimental signals using three performance metrics: the root mean square error ([Formula: see text]), the improvement in signal-to-noise ratio ([Formula: see text]), and the percentage reduction in the correlation coefficient ( η). Other methods, including traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method, were examined for comparison. The proposed method achieved the best performance to remove BW or WGN. It also effectively reduced PLI noise when the signal-to-noise ratio (SNR) was low. The SNR was improved by 18.6, 19.2, and 8.0dB for EMG signals corrupted with PLI, BW, and WGN at -6dB SNR, respectively. The experimental results illustrated that noise was completely removed from resting states, and obvious spikes were distinguished from action states. For two of the ten subjects, the improved SNR reached 20dB. This study explores the special characteristics of VMD and demonstrates the feasibility of using the VMD-based filter to denoise EMG signals. The proposed filter is efficient at removing three categories of noise and can be used for any application that requires EMG signal filtering at the preprocessing stage, such as gesture recognition and EMG decomposition.

A review on ECG filtering techniques for rhythm analysis

Electrocardiogram (ECG) signal recording is a challenging task in the field of biomedical engineering. ECG is the cardiac recording of systematic electrical activity arising from the electro-physiological rhythm of the heart muscle. But, during processing, the ECG signal is contaminated with different types of noise in the medical environment. An immense task is the separation of the preferred signal from noises caused by artifacts like muscle noise, power line interference (PLI), baseline wandering (BW), and motion artifacts (MA). Hence, our paper focuses on 50 Hz PLI which is a major artifact/noise affecting the recorded ECG signal. This paper comprehensively reviews fundamental concepts of different denoising techniques. Some of the pioneers’ works are also concisely explained in the paper. Further, in this work, comparative analysis is carried out using notch filter, adaptive filter, discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for filtering 50 Hz PLI noise. A considerable improvement in signal-to-noise ratio (SNR) can be observed from the results when compared with SNR input and SNR output values. Performance comparisons of all the four techniques are also analyzed based on variations in noise frequency. The simulations were carried out in the environment of MATLAB 2019b®. This work epitomizes the significance of our quantitative evaluation, in which adaptive filters are found to perform better with respect to the SNR, whereas DWT performs better with assessment of mean square error (MSE).

Fully Adaptive Denoising of ECG Signals Using Empirical Mode Decomposition with the Modified Indirect Subtraction and the Adaptive Window Techniques

Electrocardiogram (ECG) is one of the major methods for the diagnosis of heart malfunctions. ECG signals are susceptible to both high-frequency and low-frequency noises such as electromyography (EMG), power line interference (PLI) and baseline wander noises, respectively. These noises deteriorate the quality of ECG signals and challenge the proper identification of heart illnesses. In this article, we report an improved fully adaptive method for canceling high-frequency noises using the empirical mode decomposition (EMD) with the modified indirect subtraction and adaptive window techniques. As high-frequency noises are approximately of zero mean and lower-order intrinsic mode functions (IMFs) contain high-frequency noises, a statistical test is performed to determine whether a particular combination of IMFs has zero mean. First, to remove the PLI noise, the modified indirect subtraction technique is used. The sum of IMFs which are dominated by the noise is passed to a Butterworth band-pass filter, and the resultant filtered signal is subtracted directly from the noisy ECG signal. Then, the EMG noise is removed by an improved adaptive window-based noise reduction technique. By exploiting this technique, in contrast to common EMD-based methods, the duration of the QRS complex is computed regarding the location of the peak of the R wave and the widest possible QRS complex which makes the proposed technique applicable to all types of ECG signals. The quantitative results of simulations performed on several records from the MIT–BIH arrhythmia database prove the better performance of the proposed method than the compared methods at different noise levels.

ECG Signal De-noising based on Adaptive Filters

Denoising a signal is one of the most important tasks in signal processing. Electrocardiogram (ECG) test gives more efficient result to analyze the heart diseases. The amplitude and frequency of the ECG signals are added with various noises and that may lead to a wrong analysis of ECG or it is difficult to interpret and quality is degraded. In this paper three different noises are added to raw ECG signal, Power-line Interference noise (PLI), Baseline Wandering (BW) noise and Composite Noise (CN). The noisy signal is pre-processed using bandpass filter, low-frequency ECG signal is selected by applying DWT, CEEMD (Complementary Ensemble Empirical Mode Decomposition), LMS (Least Mean Square) and NLMS (Normalized Least Mean Square) are the different filtering techniques used to denoised. To increase the signal quality, the denoised ECG is applied to Kalman Smoother. Inverse wavelet transforms, which reconstruct signal without destructing features of ECG signal. The simulation result shows that the proposed system with better performance compared to another traditional system in terms of Signal to Noise Ratio (SNR), Correlation Coefficient (CCR), Percentage Root mean square Difference (PRD) and the Mean Square Error (MSE).

Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition

Abstract In this paper, a novel method is proposed for baseline wander (BW) and power line interference (PLI) removal from electrocardiogram (ECG) signals. The proposed methodology is based on the eigenvalue decomposition of the Hankel matrix. It has been observed that the end-point eigenvalues of the Hankel matrix formed using noisy ECG signals are correlated with BW and PLI components. We have proposed a methodology to remove BW and PLI noise by eliminating eigenvalues corresponding to noisy components. The proposed concept uses one-step process for removing both BW and PLI noise simultaneously. The proposed method has been compared with other existing methods using performance measure parameters namely output signal to noise ratio (SNRout), and percent root mean square difference (PRD). Simulation results validate the better performance of the proposed method than compared methods at different noise levels. The proposed method is suitable for preprocessing of ECG signals.

Power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal

With the urbanization in recent years, the power line interference noise in electromagnetic signal is increasing day by day, and has gradually become an unavoidable component of noises in magnetotelluric signal detection. Therefore, a kind of power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal is put forward in this paper. The method first uses wavelet decomposition to change single-channel signal into multi-channel signal, and then takes advantage of blind source separation principle of independent component analysis to eliminate power line interference noise. There is no need to choose the layer number of wavelet decomposition and the wavelet base of wavelet decomposition according to the observed signal. On the treatment effect, it is better than the previous power line interference removal method based on independent component analysis. Through the de-noising processing to actual magnetotelluric measuring data, it is shown that this method makes both the apparent resistivity curve near 50 Hz and the phase curve near 50 Hz become smoother and steadier than before processing, i.e., it effectively eliminates the power line interference noise.

Performance Analysis of Robust Recursive Least Square and its Lattice Structure for Noise Cancellation in ECG Signals

Bio signals are non-stationary signals corrupted by various environmental noises like Power Line Interference (PLI), Baseline Drift, Instrumentation noise generated by electronic devices, Motion artifacts, Electrosurgical Noise, Electrode Motion (EM) noise. Thus removal of the environmental noises becomes a challenging task. The filters with fixed coefficients cannot be applied because the human behavior is not exactly known at any point of time. An adaptive filter has flexible ability to modify the system parameters and adjusts the input signal to bring out optimal performance. This paper introduces the application of Robust Recursive Least Algorithm (RRLS) for adaptive noise cancellation. The Robust RLS algorithm is used to find the optimal value of the regularization parameter (delta) and forgetting factor (lambda). By maintaining a variable forgetting factor the convergence of the algorithm is faster. Architectural modifications such as direct form and transpose form structures are also carried out in filter structure to analyze the delay. To reduce the computational complexity of the RRLS algorithms Lattice RLS Structures are also studied for its performance. Accordingly 4 tap, 8 tap and 16 tap RRLS filters in both direct form and transpose forms are designed and implemented in Virtex5 FPGA kit. Also 4 tap, 8 tap and 16 tap lattice filters and its pipelined version are designed and implemented. The designed filters are tested using various ECG signals taken from MIT BIH database. Result shows an improvement of 10.88% of SNR for PLI Noise and 12.3% of SNR for EM Noise compared to RRLS Filter. From the overall analysis, the number of LUT‘S required for Lattice RLS has been reduced by 12.1% for 8-Tap compared to Robust RLS filter. It is also observed that the combinational path delay of pipelined lattice RLS is reduced by 80.7% for 8-Tap compared to Robust RLS filter.

SWARM ALGORITHM BASED ADAPTIVE FILTER DESIGN TO REMOVE POWER LINE INTERFERENCE FROM ECG SIGNAL

ECG signal is having wide importance in the biomedical field, but for proper diagnosis of ECG always a noise free ECG signal is needed. Many researchers have already developed filters for getting appropriate desirable ECG signal and till today many researchers are still developing different filters using different algorithms in order to get clearer ECG signal for proper diagnosis. Noises and Interferences get added in the ECG by different ways, at the time of ECG Acquisition or at the time of ECG signal recording. In this paper newly adapted algorithm is used for the filtering of ECG signal that is a Swarm algorithm which is used for the Error signal optimization from the original corrupted ECG signal. This algorithm is implemented with Adaptive filter to removes Power Line Interference noise having Frequency component of 50 Hz. The ECG signal considered may be retrieved from ECG acquisition system or from MIT-BIH database.