Electromyogram Artifacts Research Articles

MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals.

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models' performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.

A novel method based on shifted rank-1 reconstruction for removing EMG artifacts in ECG signals

Electrocardiogram (ECG) signal is an essential feature of human health monitoring and detection. Electromyogram (EMG) artifacts will be involved as the noise distortion and make it difficult for the doctor’s diagnosis. By comparing with the characteristics of these two signals, ECG signals have periodicity while EMG artifacts are random. However, they overlap in the frequency domain. According to the periodicity of ECG signals, several methods of extracting ECG signals from EMG artifacts are proposed. The core of ECG extraction is to form a rank-1 trajectory matrix for reconstruction. However, due to the EMG artifacts, most formed matrices are not strict rank-1 matrices, and the length of traversal segment selected for pure ECG reconstructions is not easily determined. Therefore, a new ECG extraction method based on shifted rank-1 reconstruction is proposed. Given an approximated traversal segment length, the trajectory matrix is constructed. Through the optimization of lambda in the low rank matrix, the constructed approximation matrix is approximated by shift vectors to obtain a strict rank-1 matrix. Pure ECG signals can be reconstructed via the singular values of shifted matrix. The validation of the proposed method is made by applying the algorithm to ECG records from four different databases. The quantitative and qualitative analysis are carried out and compared with other methods. The results indicate that the proposed SR1 method can remove EMG artifacts and does not require any specific traversal segment lengths to extract clean ECG signals. The QRS complexes and ST segments can be retained in reconstructed ECG signals.

Heartbeat detection from single-lead ECG contaminated with simulated EMG at different intensity levels: A comparative study

Electrocardiogram (ECG) signals quality can be seriously affected by artifacts such as electromyogram (EMG), negatively impacting its clinical value. Various denoising methods have been proposed to address this issue in the literature. We examined how simulated EMG artifacts affect the heartbeat detection performance of different ECG denoising pipelines at four EMG intensity levels (N1,…,N4), by adding simulated EMG to ECG signals from two different datasets: MIT-BIH arrhythmia dataset (dataset 1) and data we collected from 20 healthy participants (dataset 2). Five different pipelines were investigated: CEEMDAN (Cd), CEEMDAN-ICA (Cd-I), SWT(WT), CEEMDAN-SWT (Cd-W), and CEEMDAN-ICA-SWT (Cd-I-W). For dataset 1, Cd and WT provided the best heartbeat detection performance at noise levels N1–N2 and N3–N4, respectively. For dataset 2, WT and Cd_I performed best at N1, while WT performed best at N2–N4. We further investigated the robustness of the pipelines against varying EMG intensity. We observed that the pipelines involving SWT (WT, Cd-W, Cd-I-W) considerably outperformed others in robustness against noise. Cd-I-W and WT were the most robust pipelines for dataset 1 and 2, respectively. As such, the potential artifact level in the environment of ECG should be assessed for selecting an appropriate denoising method.

An Adaptive Spatial Filtering Method for Multi-channel EMG Artifact Removal during Functional Electrical Stimulation with Time-variant Parameters.

Removing the stimulation artifacts evoked by the functional electrical stimulation (FES) in electromyogram (EMG) signals is a challenge. Previous researches on stimulation artifact removal have focused on FES modulation with time-constant parameters, which has limitations when there are time-variant parameters. Therefore, considering the synchronism of muscle activation induced by FES and the asynchronism of muscle activation induced by proprioceptive nerves, we proposed a novel adaptive spatial filtering method called G-S-G. It entails fusing the Gram-Schmidt orthogonalization (G-S) and Grubbs criterion (G) algorithms to remove the FES-evoked stimulation artifacts in multi-channel EMG signals. To verify this method, we constructed a series of simulation data by fusing the FES signal with time-variant parameters and the voluntary EMG (vEMG) signal, and applied the G-S-G method to remove any FES artifacts from the simulation data. After that, we calculated the root mean square (RMS) value for both preprocessed simulation data and the vEMG data, and then compared them. The simulation results showed that the G-S-G method was robust and effective at removing FES artifacts in simulated EMG signals, and the correlation coefficient between the preprocessed EMG data and the recorded vEMG data yielded a good performance, up to 0.87. Furthermore, we applied the proposed method to the experimental EMG data with FES-evoked stimulation artifact, and also achieved good performance with both the time-constant and time-variant parameters. This study provides a new and accessible approach to resolving the problem of removing FES-evoked stimulation artifacts.

Singular Spectrum Analysis Based EMG Artifact Removal from ECG Signal

Electromyogram (EMG) or muscle artifacts frequently affect electrocardiogram (ECG) readings. These artifacts make the required information in the ECG signal difficult to see. In this study, we introduced the singular spectrum analysis (SSA), a powerful subspace-based method for removing EMG artifacts from ECG data. In order to effectively extract the desired component from the tainted ECG data, we presented a new grouping approach and set a threshold. First, a process known as embedding converts a single channel signal into several channels of signals or data. The orthogonal eigenvectors are then calculated using singular value decomposition(SVD) from the multichannel data's covariance matrix. A threshold is selected to locate these eigenvectors, which are utilized to generate the required subspace. After locating the subspace, the multichannel data is simply projected into it, followed by a method called diagonal averaging which will create the original time series and extract the ECG signals. Keywords: Electrocardiogram, EMG artifact, Singular Spectrum Analysis, Embedding, SVD, Mobility.

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)-A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG.

Electroencephalographic (EEG) recordings are often contaminated by electromyographic (EMG) artifacts, especially when recording during movement. Existing methods to remove EMG artifacts include independent component analysis (ICA), and other high-order statistical methods. However, these methods can not effectively remove most of EMG artifacts. Here, we proposed a modified ICA model for EMG artifacts removal in the EEG, which is called EMG Removal by Adding Sources of EMG (ERASE). In this new approach, additional channels of real EMG from neck and head muscles (reference artifacts) were added as inputs to ICA in order to “force” the most power from EMG artifacts into a few independent components (ICs). The ICs containing EMG artifacts (the “artifact ICs”) were identified and rejected using an automated procedure. ERASE was validated first using both simulated and experimentally-recorded EEG and EMG. Simulation results showed ERASE removed EMG artifacts from EEG significantly more effectively than conventional ICA. Also, it had a low false positive rate and high sensitivity. Subsequently, EEG was collected from 8 healthy participants while they moved their hands to test the realistic efficacy of this approach. Results showed that ERASE successfully removed EMG artifacts (on average, about 75% of EMG artifacts were removed when using real EMGs as reference artifacts) while preserving the expected EEG features related to movement. We also tested the ERASE procedure using simulated EMGs as reference artifacts (about 63% of EMG artifacts removed). Compared to conventional ICA, ERASE removed on average 26% more EMG artifacts from EEG. These findings suggest that ERASE can achieve significant separation of EEG signal and EMG artifacts without a loss of the underlying EEG features. These results indicate that using additional real or simulated EMG sources can increase the effectiveness of ICA in removing EMG artifacts from EEG. Combined with automated artifact IC rejection, ERASE also minimizes potential user bias. Future work will focus on improving ERASE so that it can also be used in real-time applications.

Refinement of High-Gamma EEG Features From TBI Patients With Hemicraniectomy Using an ICA Informed by Simulated Myoelectric Artifacts.

Recent studies have shown the ability to record high-γ signals (80–160 Hz) in electroencephalogram (EEG) from traumatic brain injury (TBI) patients who have had hemicraniectomies. However, extraction of the movement-related high-γ remains challenging due to a confounding bandwidth overlap with surface electromyogram (EMG) artifacts related to facial and head movements. In our previous work, we described an augmented independent component analysis (ICA) approach for removal of EMG artifacts from EEG, and referred to as EMG Reduction by Adding Sources of EMG (ERASE). Here, we tested this algorithm on EEG recorded from six TBI patients with hemicraniectomies while they performed a thumb flexion task. ERASE removed a mean of 52 ± 12% (mean ± S.E.M) (maximum 73%) of EMG artifacts. In contrast, conventional ICA removed a mean of 27 ± 19% (mean ± S.E.M) of EMG artifacts from EEG. In particular, high-γ synchronization was significantly improved in the contralateral hand motor cortex area within the hemicraniectomy site after ERASE was applied. A more sophisticated measure of high-γ complexity is the fractal dimension (FD). Here, we computed the FD of EEG high-γ on each channel. Relative FD of high-γ was defined as that the FD in move state was subtracted by FD in idle state. We found relative FD of high-γ over hemicraniectomy after applying ERASE were strongly correlated to the amplitude of finger flexion force. Results showed that significant correlation coefficients across the electrodes related to thumb flexion averaged ~0.76, while the coefficients across the homologous electrodes in non-hemicraniectomy areas were nearly 0. After conventional ICA, a correlation between relative FD of high-γ and force remained high in both hemicraniectomy areas (up to 0.86) and non-hemicraniectomy areas (up to 0.81). Across all subjects, an average of 83% of electrodes significantly correlated with force was located in the hemicraniectomy areas after applying ERASE. After conventional ICA, only 19% of electrodes with significant correlations were located in the hemicraniectomy. These results indicated that the new approach isolated electrophysiological features during finger motor activation while selectively removing confounding EMG artifacts. This approach removed EMG artifacts that can contaminate high-gamma activity recorded over the hemicraniectomy.

Application of Stockwell Transform and Shannon Energy for Pace Pulses Detection in a Single-Lead ECG Corrupted by EMG Artifacts

Electrocardiogram (ECG) analysis is important for the detection of pace pulse artifacts, since their existence indicates the presence of a pacemaker. ECG gives information on the proper functionality of the device and could help to evaluate the reaction of the heart. Beyond the challenges related to the diversity of ECG arrhythmias and pace pulses, the existence of electromyogram (EMG) noise could cause serious problems for the correct detection of pace pulses. This study reveals the potential of a methodology based on Stockwell transformation (S-transform), subsequent Shannon energy calculation and a threshold-based rule for pace artifact detection in a single-lead ECG corrupted with EMG noise. The design, validation and test are performed on a large, publicly available artificial database acquired with high amplitude and time resolution. It includes various combinations of ECG arrhythmias and pace pulses with different amplitudes, rising edges and total pulse durations, as well as timing that corresponds to different pacemaker modes. The training was done over 312 (ECG + EMG) signals. The method was validated on 390 clean ECGs and independently tested on 312 (ECG + EMG) and 390 clean ECGs. The achieved accuracy over the test dataset was Se = 100%, PPV = 98.0% for ECG corrupted by EMG artifacts and Se = 99.9%, PPV = 98.3% for clean ECG signals. This shows that, despite EMG artifacts, the S-transform could distinctly localize the pace pulse positions and, together with the applied ShE, could provide precise pace pulses detection in the time domain.

A novel method based on Adaptive Periodic Segment Matrix and Singular Value Decomposition for removing EMG artifact in ECG signal

The Electrocardiogram (ECG) signals are usually used to detect and monitor human health. However, the Electromyogram (EMG) artifacts also can be obtained during measurement, these make difficult for doctors in correct diagnosis. In general, ECG signals are periodic while EMG artifacts are non-stationary and overlapped in the frequency domain. According to these characteristics, it is necessary to extract clean ECG signals from EMG artifacts by using the periodic separation method. A novel Adaptive Periodic Segment Matrix (APSM) based on Singular Value Decomposition (SVD) is proposed for extracting clean ECG signals from EMG artifacts. Firstly, a periodic segment estimation method is proposed by obtaining an average periodic length and RR intervals constraint via envelope spectrum of the measured signal. Secondly, the R wave peaks and their positions of the ECG signals are detected by these. After that, APSM with rank one is formed using R wave peaks and the calculated RR intervals constraint, then SVD is processed on this matrix, the restructured ECG signals will be obtained by the first maximal singular value of the formed matrix. The validation of proposed method is made by applying the algorithm to ECG records from different four databases. Quantitative and qualitative analyses have been made and compared with other methods. The results show that the proposed APSM-SVD method is effective for EMG artifacts removal and clean ECG signals extraction. The R peak, P wave, QRS complex and ST segment can be preserved in reconstructed ECG signals.

Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis.

Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.

Removing Muscle Artifacts From EEG Data via Underdetermined Joint Blind Source Separation: A Simulation Study

Electroencephalography (EEG) recordings are often contaminated by artifacts from electromyogram (EMG). This artifact not only affects the visual analysis but also strongly impedes its various usages in biomedical research. With a sufficient number of EEG recordings, numerous blind source separation (BSS) methods can be applied to suppress or remove such EMG artifacts. However, in many practical applications (e.g., ambulatory health-care monitoring), the number of EEG sensors is often limited, while conventional BSS methods (e.g., independent component analysis) may fail to work in such cases. Considering the increasing need for acquiring EEG signals in ambulatory environments, we propose a novel underdetermined joint BSS method to remove EMG artifacts from EEG data with a limited number of EEG sensors. The performance of the proposed method is evaluated through numerical simulations in which EEG recordings are contaminated with muscle artifacts. The results demonstrate that the proposed method can effectively remove muscle artifacts while preserving EEG signals successfully.

Remove Diverse Artifacts Simultaneously From a Single-Channel EEG Based on SSA and ICA: A Semi-Simulated Study

Electroencephalogram (EEG) signals are often contaminated with diverse artifacts, such as electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. These artifacts make subsequent EEG analysis inaccurate and prevent practical usage. Recently, the use of wearable EEG devices in ambulatory systems has been developed. For practical reasons, these systems usually contain a single EEG channel. Several studies have proposed to combine single-channel decomposition methods with blind source separation (BSS) methods to denoise the single-channel EEG. However, the existing methods have their own limitations since most of them only focus on removing one single kind of artifacts. Unfortunately, the EEG is prone to be contaminated by various kinds of artifacts simultaneously. Yet to our knowledge, there are no existing methods to remove diverse artifacts simultaneously from the single-channel EEG. To address this issue, we propose an effective method to remove diverse artifacts simultaneously for the single-channel EEG case. This method is a combination of singular spectrum analysis (SSA) and second-order blind identification (SOBI) method. We conduct a semi-simulated study to investigate all possible cases of the single-channel EEG been contaminated by EMG, EOG, and ECG artifacts. The results show that the proposed method can successfully remove diverse artifacts from the single-channel EEG. It is a promising tool for biomedical signal processing applications.

Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising

The physiological artifacts such as electromyogram (EMG) and electrooculogram (EOG) remain a major problem in electroencephalogram (EEG) research. A number of techniques are currently in use to remove these artifacts with the hope that the process does not unduly degrade the quality of the obscured EEG. In this paper, a new method has been proposed by combining two techniques: a canonical correlation analysis (CCA) followed by a stationary wavelet transform (SWT) to remove EMG artifacts and a second-order blind identification (SOBI) technique followed by SWT to remove EOG artifacts. The simulation results show that these combinations are more effective than either using the individual techniques alone or using other combinations of techniques. The quality of the artifact removal is evaluated by calculating correlations between processed and unprocessed data, and the practicability of the technique is judged by comparing execution times of the algorithms.

Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system

A new way of removing electrooculogram (EOG) and electromyogram (EMG) artifacts from an electroencephalogram (EEG) was developed that involves combining an adaptive neural fuzzy inference system (ANFIS) and a functional link neural network (FLNN) to construct a filter. The ANFIS is divided into a nonlinear antecedent part and a linear consequent part; and the consequent part is replaced with the FLNN to enhance the nonlinear approximation ability of the method. An adaptive filtering algorithm adjusts the parameters of the fuzzy inference and neural network. Verification of the method showed that it was effective in removing EOG and EMG artifacts from an EEG. A comparison showed that this method provided better performance than an ANFIS, an RBF-ANFIS, or an adaptive FL-BPNN.

Filtering of electromyogram artifacts from the electrocardiogram

Electromyogram (EMG) artifacts often contaminate the electrocardiogram (ECG). They are more difficult to suppress or eliminate, compared for example to the power line interference, due to their random character and to the considerable overlapping of the frequency spectra of ECG and EMG signals obtained from the same pair of electrodes. The usually applied low-pass filtering (cutoff frequency of minimum 35 Hz) results in limited suppression of the EMG artifact and considerable reduction of sharp Q, R and S ECG wave amplitudes. A solution to this problem is proposed by applying approximation filtering with dynamically varied number of samples and weighting coefficients, depending on the ECG signal slope. The slope measure used is the absolute value of the product of the tilts of two adjacent 10 ms segments sliding along the signal. The results obtained show a slight widening of some sharper QRS complexes, but a virtual preservation of their amplitudes and a considerable reduction of the EMG artifact.