EMG Noise Research Articles

Noise Suppression of ECG Signal Using Optimized Digital Butterworth Bandpass Filter

ECG devices have been widely utilized by medical experts for cardiac health detection. It generates an analog signal that has intrinsic characteristics represented by PQRST waves. Each wave is useful for diagnostic purposes. However, ECG signal is prone to noise, particularly EMG noise. This noise occurs as a result of muscle contraction or sudden body movement of a subject. Several studies have been proposed to suppress the EMG noise on ECG. However, the complexity of an algorithm is a challenging task to be solved. Considering this problem, this research aims to suppress EMG noise using a low-complexity algorithm. To achieve this objective, a method of Butterworth bandpass filter DF-II has been proposed. The findings demonstrate that the proposed method improves SNR and signal power by 0.498dB and 0.006dBm/Hz respectively. It also demonstrates the capability to perform efficiently on a microcontroller unit.

Discrete wavelet transform based processing of embroidered textile-electrode electromyography signal acquired with load and pressure effect

The diagnosis of neuromuscular diseases is complicated by overlapping symptoms from other conditions. Textile-based surface electromyography (sEMG) of skeletal muscles, offer promising potential in diagnosis, treatment, and rehabilitation of various neuromuscular disorders. However, it is important to consider the impact of load and pressure on EMG signals, as this can significantly affect the signal’s accuracy. This study seeks to investigate the influence of load and pressure on EMG signals and establish a processing framework for these signals in the diagnosis of neuromuscular diseases. The sEMG data were collected from healthy subjects using a textile electrode developed from polyester multi-filament conductive hybrid thread (CleverTex). The textrode was embroidered directly on an elastic bandage (Velcro® strap) placed on volunteer’s muscles while different activities were performed with varying loads and pressure. The collected data were pre-processed using standard techniques of the discrete wavelet transform to remove noise and artifacts. The performance of the proposed denoising algorithm was evaluated using the signal-to-noise ratio (SNR), percentage root mean square difference (PRD), and root mean square error (RMSE). Various signal processing approaches (filters) were considered and the results were compared with the proposed EMG noise reduction algorithms. Based on the experimental results, the fourth level of decomposition for the sym5 wavelets with the Rigrsure threshold method achieved the highest signal-to-noise ratio (SNR) values of 16.69 and 21.91, for soft and hard thresholding functions, respectively. The SNR values of 22.11, 21.54, and 2.78 at three different pressure levels 5 mmHg, 10 mmHg, and 20 mmHg, respectively, indicate the superior performance of wavelet multiresolution filter in de-noising applications. The results of this study suggest that our methodology is effective, precise, and reliable for analysing sEMG data and provide insights into both physiological and pathological neuromuscular conditions.

Noise Reduction Techniques in ECG Signal

The problem of noise interference in ECG signals has been addressed in this paper. Specifically, a method has been developed to filter out Electromyography noise (EMG) from ECG signals. A dataset of ECG signals with varying levels of EMG noise has been collected using the MIT-BIH dataset. An algorithm has been designed and implemented using the DA FIR filter coupled with Kaiser windowing technique to filter out the noise. The algorithm has been tested on the collected dataset using MATLAB. The performance of the algorithm has been evaluated by calculating the Signal-to-Noise Ratio (SNR) and the Mean Squared Error (MSE). The effectiveness of the algorithm in reducing the EMG noise in the ECG signals has been demonstrated by the results. The algorithm's limitations and future work were discussed in this paper. Interesting future works could include using other filtering techniques to enhance the performance or deep learning techniques to improve noise cancellation. Overall, the effectiveness of using signal processing techniques to filter out EMG noise from ECG signals has been demonstrated and resulted in clearer and more accurate signals for diagnostic purposes.

Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network

Cardiovascular disease (CVD) has been identified as a threat to human life for decades, with the majority of individuals dying as a result of delayed diagnosis and treatment. An electrocardiogram (ECG) plays a vital role in the prognosis of such an ailment. The presence of noise and artifacts complicates the accurate detection and identification of CVD. As a result, reliable signal recovery tasks necessitate noise removal, which is an inverse problem. The main noises present in electrocardiogram (ECG) signals are EMG noise, electrode motion artifact noise. In this paper, radial basis function (RBF) and multi swarm optimization neural network (MSONN) are used to denoise the ECG signal. The cut-off frequency is calculated using a low-pass filter. By using, fuzzy FIR filtering technique baseline wander noises can be removed. Results show that MOS based approach outperforms existing approaches in terms of accuracy and is observed to be 87% even when the dataset size is small. Further, noises if any exists are also removed by the use of cascaded multiplier less Fuzzy FIR filters

Application of an EMG interference filtering method to dynamic ECGs based on an adaptive wavelet-Wiener filter and adaptive moving average filter

In dynamic ECG signals, EMG interference is a common type of noise and overlaps with the ECG signal spectrum. Herein, we propose an EMG-filtering method that combines an adaptive wavelet-Wiener filter and an adaptive moving average filter. First, the R-peaks detection method is used to divide the signal into individual heartbeats, and wavelet threshold processing is used to evaluate signal-to-noise ratio. The wavelet-Wiener filter’s parameters are then set adaptively according to the SNR, and the resulting adaptive wavelet-Wiener filter is applied to each heartbeat. Next, the low frequency components between two heartbeats are windowed to calculate standard deviation, and the order of the moving average filter is set adaptively according to the estimated SNR and standard deviation. Finally, a moving average filter is applied to the low frequency components to filter residual EMG noise. Before and after applying the moving average filter, splicing and discarding are performed on both sides of the low frequency components, effectively preventing distortion at the transition between the high and low frequency components. We evaluated the proposed method’s performance using different ECG databases, including arrhythmia and dynamic ECG signals with motion artifacts, and compared its performance with that of other methods. Experimental results show that the proposed method can significantly improve SNR and effectively preserve clinical information, especially in environments with severe noise. The proposed method utilizes the advantages of both applied techniques to reduce the noise in ECG signals with minimal distortion and can be used as an effective tool for denoising ECG signals.

EEG signal artefact removal using flower pollination fractional calculus optimisation

PurposeThe main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially, pre-processing is done on EEG signal for quality improvement. Then, by using wavelet transform (WT) feature extraction is done. The artefacts present in the EEG are removed using deep convLSTM. This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.Design/methodology/approachNowadays' EEG signals play vital role in the field of neurophysiologic research. Brain activities of human can be analysed by using EEG signals. These signals are frequently affected by noise during acquisition and other external disturbances, which lead to degrade the signal quality. Denoising of EEG signals is necessary for the effective usage of signals in any application. This paper proposes a new technique named as flower pollination fractional calculus optimisation (FPFCO) algorithm for the removal of artefacts from EEG signal through deep learning scheme. FPFCO algorithm is the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM. The existed FPO algorithm is used for solution update through global and local pollinations. In this case, the fractional calculus (FC) method attempts to include the past solution by including the second order derivative. As a result, the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization (FPO) method. Initially, 5 EEG signals are contaminated by artefacts such as EMG, EOG, EEG and random noise. These contaminated EEG signals are pre-processed to remove baseline and power line noises. Further, feature extraction is done by using WT and extracted features are applied to deep convLSTM, which is trained by proposed fractional calculus based flower pollination optimisation algorithm. FPFCO is used for the effective removal of artefacts from EEG signal. The proposed technique is compared with existing techniques in terms of SNR and MSE.FindingsThe proposed technique is compared with existing techniques in terms of SNR, RMSE and MSE.Originality/value100%.

Cat Swarm Fractional Calculus optimization-based deep learning for artifact removal from EEG signal

ABSTRACT Electroencephalogram (EEG) signals are commonly used in analysing the brain activity. The EEG signals have small amplitude and hence, they are often affected by the artefacts. For the efficient processing, it is necessary to remove the artefacts from the EEG signals. This paper develops a technique through deep learning scheme for removing the artefacts present in the EEG signal. Initially, the EEG signals are pre-processed and provided to the feature extraction process, where the wavelet features are extracted from the signal by applying the wavelet transform. The extracted features are provided to the proposed classifier, namely deep-ConvLSTM, for removing the artefacts from the EEG signal. Here, the deep learner is trained based on the proposed Cat Swarm Fractional Calculus Optimisation (CSFCO) algorithm, which is the integration of Cat Swarm Optimisation (CSO) and Fractional Calculus (FC). Experimentation of the proposed technique is carried out by introducing artefacts, such as ECG, EMG, EOG and random noise on the EEG signal. Simulation results carried out on the proposed deep-ConvLSTM depict that the proposed framework has better performance than the comparative techniques with the values of 3888.362, 62.356, and 69.939 dB, for the MSE, RMSE, and SNR, respectively.

ICA-based muscle-tendon units localization and activation analysis during dynamic motion tasks.

This study proposed an independent component analysis (ICA)-based framework for localization and activation level analysis of muscle-tendon units (MTUs) within skeletal muscles during dynamic motion. The gastrocnemius muscle and extensor digitorum communis were selected as target muscles. High-density electrode arrays were used to record surface electromyographic (sEMG) data of the targeted muscles during dynamic motion tasks. First, the ICA algorithm was used to decompose multi-channel sEMG data into a weight coefficient matrix and a source matrix. Then, the source signal matrix was analyzed to determine EMG sources and noise sources. The weight coefficient vectors corresponding to the EMG sources were mapped to target muscles to find the location of the MTUs. Meanwhile, the activation level changes in MTUs during dynamic motion tasks were analyzed based on the corresponding EMG source signals. Eight subjects were recruited for this study, and the experimental results verified the feasibility and practicality of the proposed ICA-based method for the MTUs' localization and activation level analysis during dynamic motion. This study provided a new, in-depth way to analyze the functional state of MTUs during dynamic tasks and laid a solid foundation for MTU-based accurate muscle force estimation, muscle fatigue prediction, neuromuscular control characteristic analysis, etc.

High Frequency Electromyogram Noise Removal from Electrocardiogram Using FIR Low Pass Filter Based on FPGA

With improvements in capacity and performance and a decrease in cost, FPGAs have become a viable solution for making custom chips and programmable DSP devices. This paper presents an efficient implementation of Finite Impulse Response Filter (FIR) using Distributed Arithmetic (DA) architecture based on FPGA with the help of Xilinx system generator software. Here, the multipliers in FIR filter are replaced with multiplier less DA based technique to remove high frequency Electrocardiogram (EMG) noise from ECG signal. As digital filters plays very significant role in the analysis of the low frequency components in Electrocardiogram (ECG) signal. The ECG is susceptible to noise and it is essential to remove the noise to support decision making for specialist and automatic heart disorder diagnosis systems. We proposed that the signals under experiment has been added with muscle noise and after applying different FIR method, the signals according to signal noise ratio (SNR) and MSE(mean square error) are evaluated. It introduces an effective technique for the denoising of ECG signals corrupted by High frequency muscles contraction noise. The performances of the system were evaluated using the Massachusetts Institute of Technology University and Beth Israel Hospital (MIT-BIH) database. Muscle noise is taken from MIT-BIH noise stress database . Simulation results shows that High frequency EMG noise from ECG was removed effectively by using FIR low pass filter. The implementation is done on a Xilinx chip of Spartan 3E XC3S500e-4fg320 using Xilinx system generator 10.1 with Matlab version7.4.0 (2007a).

ECG Signal De-noising with Asynchronous Averaging and Filtering Algorithm

In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.

Dynamic powerline interference subtraction from biosignals

The performance of a previously developed and widely used method for powerline interference subtraction from the ECG is slightly reduced in the presence of continuous well-expressed EMG noise. Applying automatic adaptation of the 'linearity criterion' value in accordance to the ECG/noise ratio, the best conditions for application of the procedure can be obtained. The proposed method allows for reduction of possible distortions when applied on noise-free ECG and enhances its efficiency in the presence of non-powerline noise. Apart from ECG, it is applicable to impedance-cardiogram, plethysmogram, EEG and other biosignals.

Comparing stress ECG enhancement algorithms

There are two predominant types of noise that contaminate the electrocardiogram (EGG) acquired during a stress test: the baseline wander noise (BW) and electrode motion artifact, and electromyogram-induced noise (EMG). BW noise is at a lower frequency, caused by respiration and motion of the subject or the leads. The frequency components of BW noise are usually below 0.5 Hz, and extend into the frequency range of the ST segment during a stress test. EMG noise, on the other hand, is predominantly at higher frequencies, caused by increased muscle activity and by mechanical forces acting on the electrodes. The frequency spectrum of the EMG noise overlaps that of the ECG signal and extends even higher in the frequency domain. In this article, the authors review some of the published ECG enhancing techniques to overcome the noise problems, and compare their performance on stress ECG signals under adverse noise scenarios. They also describe the filter bank-based ECG enhancing algorithm.

Noise coherence in closely-spaced electrodes: the implications for spatial averaged ECG recordings.

Ensemble signal averaging is conventionally used to reduce the noise content of ECG recordings. One shortcoming of this technique is that it removes the possibility of registering transient signals. As a consequence the simultaneous averaging of spatially related leads has been used. This relies on the recording electrodes receiving correlated signal components for reinforcement together with uncorrelated noise. We have developed a methodology for studying EMG noise reduction by spatial averaging and present results for different electrode spacings which suggest minimum separations for noise rejection.

Errors in frequency parameters of EMG power spectra

Frequency shifts in random signals, e.g., EMG or Doppler ultrasound, can be followed by monitoring one or more parameters of the power spectrum. When such a frequency parameter is determined over a finite length of the signal, a random error and sometimes a systematic error or bias are introduced. Approximate expressions, in terms of moments of the power spectrum, have been derived for bias and standard deviation of the estimates for mean frequency, zero-crossing frequency, and fractile frequency (of which the median frequency is a special case). Experimental results from surface EMG recordings of three human muscles in constant force isometric contractions were in agreement with the theoretical predictions. In this case the mean frequency had the smallest random error. It turned out that the measured values of the zero-crossing frequency can deviate considerably from the predictions by the Rice formula when the amplitude distribution is not exactly Gaussian. In the presence of noise, all frequency parameters show a systematic deviation, depending on the signal-to-noise ratio. In addition to known results on this deviation for mean and zero-crossing frequency, an exact and an approximate expression for the fractile frequency are given. In the case of EMG plus wide-band white noise, the median frequency has the best immunity to noise.

On computer recognition of nystagmus signal contaminated by EMG noise.

On computer recognition of nystagmus signal contaminated by EMG noise.

A statistical analysis of the spectral moments used in EMG tests of endurance.

EMG and bandlimited noise (BLN) signals of comparable spectra were processed to compute the centroid and median frequencies and the high/low ratio for different record lengths, signal amplitudes, and noise bandwidths. The spectral moments of EMG and BLN showed similar statistical properties, independent of record length and amplitude. The signal-to-noise ratios of the moments increased with record length. Relatively short records were sufficient to obtain low coefficients of variations especially for the centroid, which was the most stable and sensitive to changes in the underlying spectra. The high/low ratio was the least stable moment. A change of 10 and 3.9% in the centroid over successive records of overall duration of 8 and 10 s, respectively, was just sufficient to detect a spectral shift at the P less than 0.05 level. The equivalent percents for the high/low ratio were 66 and 32.5, respectively. These thresholds of significance were related to time constants of equivalent exponential trends in the spectra used to study fatigue. This relationship makes the results of the present study applicable to a wide variety of EMG tests of endurance.