scholarly journals Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4890
Author(s):  
Lin Xu ◽  
Elisabetta Peri ◽  
Rik Vullings ◽  
Chiara Rabotti ◽  
Johannes P. Van Dijk ◽  
...  
Keyword(s):  
Electrical Activity ◽  
Biceps Brachii ◽  
Signal To Noise Ratio ◽  
Surface Emg ◽  
Cardiac Electrical Activity ◽  
Emg Signal ◽  
Comparative Review ◽  
Electrocardiogram Ecg ◽  
High Pass

Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.

In-vivo evaluation of reduced-lead-systems in noninvasive reconstruction and localization of cardiac electrical activity

2015 ◽  
Author(s):  
Matthijs JM Cluitmans ◽  
Joel Karel ◽  
Pietro Bonizzi ◽  
Monique MJ de Jong ◽  
Paul GA Volders ◽  
...  
Keyword(s):  
Electrical Activity ◽  
In Vivo Evaluation ◽  
Cardiac Electrical Activity

scholarly journals Surface EMG signal normalisation and filtering improves sensitivity of equine gait analysis

2019 ◽  
Vol 15 (3) ◽  
pp. 173-185 ◽  
Author(s):  
L. St. George ◽  
S.H. Roy ◽  
J. Richards ◽  
J. Sinclair ◽  
S.J. Hobbs
Keyword(s):  
Repeated Measures ◽  
Low Frequency ◽  
Biceps Femoris ◽  
Surface Emg ◽  
Frequency Noise ◽  
Vertical Loading ◽  
Stifle Joint ◽  
Emg Signal ◽  
Stride Duration ◽  
High Pass

Low-frequency noise attenuation and normalisation are fundamental signal processing (SP) methods for surface electromyography (sEMG), but are absent, or not consistently applied, in equine biomechanics. The purpose of this study was to examine the effect of different band-pass filtering and normalisation conventions on sensitivity for identifying differences in sEMG amplitude-related measures, calculated from leading (LdH) and trailing hindlimb (TrH) during canter, where between-limb differences in vertical loading are known. sEMG and 3D-kinematic data were collected from the right Biceps Femoris in 10 horses during both canter leads. Peak hip and stifle joint angle and angular velocity were calculated during stance to verify between-limb biomechanical differences. Four SP methods, with and without normalisation and high-pass filtering, were applied to raw sEMG data. Methods 1 (M1) to 4 (M4) included DC-offset removal and full-wave rectification. Method 2 (M2) included additional normalisation relative to maximum sEMG across all strides. Method 3 (M3) included additional high-pass filtering (Butterworth 4th order, 40 Hz cut-off), for artefact attenuation. M4 included the addition of high-pass filtering and normalisation. Integrated EMG (iEMG) and average rectified value (ARV) were calculated using processed sEMG data from M1 – M4, with stride duration as the temporal domain. sEMG parameters, within M1 – M4, and kinematic parameters were grouped by LdH and TrH and compared using repeated measures ANOVA. Significant between-limb differences for hip and stifle joint kinematics were found, indicating functional differences in hindlimb movement. M2 and M4, revealed significantly greater iEMG and ARV for LdH than TrH (P<0.01), with M4 producing the lowest P-values and largest effect sizes. Significant between-limb differences in sEMG parameters were not observed with M1 and M3. The results indicate that equine sEMG SP should include normalisation and high-pass filtering to improve sensitivity for identifying differences in muscle function associated with biomechanical changes during equine gait.

scholarly journals Effects of Sampling Frequency and Sample Window Size on Median Frequency of Surface EMG

2019 ◽  
Vol 63 (1) ◽  
pp. 1369-1372
Author(s):  
Pramiti Sarker ◽  
Gary Mirka
Keyword(s):  
Biceps Brachii ◽  
Window Size ◽  
Elbow Flexion ◽  
Surface Emg ◽  
Sampling Frequency ◽  
Emg Activity ◽  
Median Frequency ◽  
Surface Electrodes ◽  
Emg Signal ◽  
Sample Window

Muscle fatigue can be evaluated through the assessment of the downward shift in the median frequency (MDF) of the electromyographic (EMG) signal collected through surface electromyography. Previous research has shown that the value of MDF may be affected by sampling parameters. The purpose of this study was to quantify the combined effect of different sampling frequencies and window sizes on the calculated MDF. A sample of 24 participants performed a simple static elbow flexion exertion (15% MVC) and the EMG activity of the biceps brachii was periodically sampled using surface electrodes for four seconds at a frequency of 4096 Hz as the biceps brachii became fatigued. These collected data were then down-sampled to create a dataset of four window sizes (1s, 2s, 3s, and 4s) and five sampling frequencies (256 Hz, 512 Hz, 1024 Hz, 2048 Hz, and 4096 Hz). Median frequencies were calculated for each combination of sampling frequency and window size and then compared with the 4096 Hz / 4 s condition (considered gold standard) and the errors were calculated. Results suggest the use of a minimum sampling frequency of 512 Hz and a window size of 4s.

scholarly journals Researches Regarding the Values of Some Electrocardiogram’S Components in Cats

2018 ◽  
Vol 75 (1) ◽  
pp. 139
Author(s):  
Cristian BROJBĂ
Keyword(s):  
Electrical Activity ◽  
Reference Values ◽  
Biological Material ◽  
Research Work ◽  
Cardiac Diseases ◽  
Cardiac Electrical Activity ◽  
Main Target ◽  
Main Components ◽  
Electrocardiogram Ecg ◽  
Ecg Interpretation

The electrocardiogram (ECG or EKG) represents the graphical recording of the cardiac electrical activity (Ghiţă et al., 2005) and it is useful in the diagnosis in some cardiac diseases (such as rhythm disorders) (Cotor and Ghiţă, 2014) or frequency disorders (Ghiţă et al., 2007).The main target of this research work was to determine the values of the main components of the ECG and the cardiac frequency. The biological material was represented by 12 healthy cats of different breeds. The values obtained in this research work can be used as reference values in ECG interpretation in cats.

scholarly journals Motor unit recruitment strategies investigated by surface EMG variables

2002 ◽  
Vol 92 (1) ◽  
pp. 235-247 ◽  
Author(s):  
Dario Farina ◽  
Mauro Fosci ◽  
Roberto Merletti
Keyword(s):  
Biceps Brachii ◽  
Motor Units ◽  
Surface Emg ◽  
Recruitment Strategy ◽  
Isometric Contractions ◽  
Size Principle ◽  
Constant Torque ◽  
Emg Signal ◽  
Power Spectral ◽  
The Central Nervous System

During isometric contractions of increasing strength, motor units (MUs) are recruited by the central nervous system in an orderly manner starting with the smallest, with muscle fibers that usually show the lowest conduction velocity (CV). Theory predicts that the higher the velocity of propagation of the action potential, the higher the power at high frequencies of the detected surface signal. These considerations suggest that the power spectral density of the surface detected electromyogram (EMG) signal may give indications about the MU recruitment process. The purpose of this paper is to investigate the potential and limitations of spectral analysis of the surface EMG signal as a technique for the investigation of muscle force control. The study is based on a simulation approach and on an experimental investigation of the properties of surface EMG signals detected from the biceps brachii during isometric linearly increasing torque contractions. Both simulation and experimental data indicate that volume conductor properties play an important role as confounding factors that may mask any relation between EMG spectral variables and estimated CV as a size principle parameter during ramp contractions. The correlation between spectral variables and CV is thus significantly lower when the MU pool is not stable than during constant-torque isometric contractions. Our results do not support the establishment of a general relationship between spectral EMG variables and torque or recruitment strategy.

EMG AMPLITUDE ESTIMATORS BASED ON PROBABILITY DISTRIBUTION FOR MUSCLE–COMPUTER INTERFACE

2013 ◽  
Vol 12 (03) ◽  
pp. 1350016 ◽  
Author(s):  
ANGKOON PHINYOMARK ◽  
FRANCK QUAINE ◽  
YANN LAURILLAU ◽  
SIRINEE THONGPANJA ◽  
CHUSAK LIMSAKUL ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Feature Space ◽  
Recognition System ◽  
Surface Emg ◽  
Classification Performance ◽  
Computer Interface ◽  
Computational Time ◽  
Flexor Muscle ◽  
Emg Amplitude ◽  
Emg Signal

To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.

scholarly journals In vivo cardiac electrical activity of nitric oxide in barium chloride treated male rats

2017 ◽  
Author(s):  
Abbas B. Q. Salihi ◽  
Mudhir S. Shekha ◽  
Peshraw S. Hamadamin ◽  
Ismail M. Maulood ◽  
Khder H. Rasul ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Electrical Activity ◽  
Barium Chloride ◽  
Male Rats ◽  
Treated Male ◽  
Cardiac Electrical Activity

Surface EMG Crosstalk Evaluated from Experimental Recordings and Simulated Signals

2004 ◽  
Vol 43 (01) ◽  
pp. 30-35 ◽  
Author(s):  
R. Merletti ◽  
B. Indino ◽  
T. Graven-Nielsen ◽  
D. Farina
Keyword(s):  
Surface Emg ◽  
Active Muscle ◽  
Single Motor Unit ◽  
Temporal Filtering ◽  
Detection Systems ◽  
Emg Signal ◽  
Spike Triggered Averaging ◽  
Frequency Components ◽  
Main Determinants ◽  
High Pass

Summary Objectives: Surface EMG crosstalk is the EMG signal detected over a non-active muscle and generated by a nearby muscle. The aim of this study was to analyze the sources of crosstalk signals in surface EMG recordings and to discuss methods proposed in the literature for crosstalk quantification and reduction. Methods: The study is based on both simulated and experimental signals. The simulated signals are generated by a structure based surface EMG signal model. Signals were recorded with both intramuscular and surface electrodes and single motor unit surface potentials were extracted with the spike triggered averaging approach. Moreover, surface EMG signals were recorded from electrically stimulated muscles. Results: From the simulation and experimental analysis it was clear that the main determinants of crosstalk are non-propagating signal components, generated by the extinction of the intracellular action potentials at the tendons. Thus, crosstalk signals have a different shape with respect to the signals detected over the active muscle and contain high frequency components. Conclusions: Since crosstalk has signal components different from those dominant in case of detection from near sources, commonly used methods to quantify and reduce crosstalk, such as the cross-correlation coefficient and high-pass temporal filtering, are not reliable. Selectivity of detection systems must be discussed separately as selectivity with respect to propagating and non-propagating signal components. The knowledge about the origin of crosstalk signal constitutes the basis for crosstalk interpretation, quantification, and reduction.

scholarly journals Validated Filter-Based Photoreceptor Count Algorithm on Retinal Heidelberg High Magnification Module™ Images in Healthy and Pathological Conditions

2021 ◽  
Vol 11 (12) ◽  
pp. 5347
Author(s):  
Timo Mulders ◽  
Patty Dhooge ◽  
Ludo van der Zanden ◽  
Carel B. Hoyng ◽  
Thomas Theelen
Keyword(s):  
Signal To Noise Ratio ◽  
Photoreceptor Cells ◽  
High Magnification ◽  
Pass Filter ◽  
Cone Mosaic ◽  
High Pass Filter ◽  
Significant Difference ◽  
High Pass ◽  
Healthy Participants

Recently introduced, the Heidelberg Engineering™ high magnification module enables in vivo visualization of cone photoreceptor cells. Currently, a reliable analysis of cone mosaic on high magnification module images is hindered by an unfavorable signal-to-noise ratio. In this paper, we describe how a novel high magnification module high-pass filter may enhance cone signals in healthy participants and patients. We compared the cone counts of our filter-based algorithm to the counts of two human graders. We found a good to excellent intragrader and intergrader correlation in both patients and healthy participants. We identified a good correlation between the average cone counts of both graders and high-pass filter cone counts in patients and healthy participants. We observed no significant difference between manual and filter-based counts via the Bland–Altman analysis. In conclusion, a quantitative cone analysis on high magnification module images is feasible manually by human graders and automatically by a filter-based algorithm. However, larger datasets are needed to improve repeatability and consistency by training human graders.

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