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.

Original waveform of lung sound crackles: a case study of the effect of high-pass filtration

1991 ◽  
Vol 71 (6) ◽  
pp. 2173-2177 ◽  
Author(s):  
T. Katila ◽  
P. Piirila ◽  
K. Kallio ◽  
E. Paajanen ◽  
T. Rosqvist ◽  
...  
Keyword(s):  
Low Frequency ◽  
Final Analysis ◽  
Sound Signal ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Lung Sound ◽  
High Pass

In lung sound research, low-frequency noise usually disturbs the sound signal being recorded. Some researchers therefore use high-pass filtration before the final analysis. In this study, the effect of digital and analog high-pass filtration on the morphology of the lung sound crackles is evaluated. The original nonprefiltered crackle waveform is presented, and the effect of the high-pass filtration on the crackle waveform characteristics is elucidated in one patient with silicoasbestosis.

Construction of a High-Pass Digital Filter from a Low-Pass Digital Filter

1994 ◽  
Vol 10 (4) ◽  
pp. 374-381 ◽  
Author(s):  
Stephen D. Murphy ◽  
D. Gordon E. Robertson
Keyword(s):  
Digital Filter ◽  
Low Frequency ◽  
Frequency Noise ◽  
Band Pass Filter ◽  
Low Frequency Noise ◽  
Pass Filter ◽  
Low Pass Filter ◽  
High Pass Filter ◽  
Low Pass ◽  
High Pass

To remove low-frequency noise from data such as DC-bias from electromyo-grams (EMGs) or drift from force transducers, a high-pass filter was constructed from a low-pass filter of known characteristics. A summary of the necessary steps required to transform the low-pass digital were developed. Contaminated EMG and force platform data were used to test the filter. The high-pass filter successfully removed the low-frequency noise from the EMG signals. The high-pass filter was then cascaded with the low-pass filter to produce a band-pass filter to enable simultaneous high- and low-frequency noise reduction.

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.

Wingate performance and surface EMG frequency variables are not affected by caffeine ingestion

2006 ◽  
Vol 31 (5) ◽  
pp. 597-603 ◽  
Author(s):  
Felicia Greer ◽  
Jacobo Morales ◽  
Michael Coles
Keyword(s):  
Body Mass ◽  
Power Output ◽  
High Intensity ◽  
Repeated Measures ◽  
Surface Emg ◽  
Knee Extensors ◽  
Caffeine Ingestion ◽  
Emg Signal ◽  
Significant Difference ◽  
Percent Decline

The ergogenic effect of caffeine and its mechanism of action on short-term, high-intensity exercise are controversial. One proposed mechanism is caffeine’s stimulatory effect on the central nervous system and thus, motor-unit excitation. The latter is non-invasively determined from surface electromyographic signal (EMG) frequency measures. The purpose of this study was to determine if power output and surface EMG frequency variables during high-intensity cycling were altered following caffeine ingestion. Eighteen recreationally active college males (mean ± SD age, 21.5 ± 1.8 y; height, 181.8 ± 0.5 cm; body mass, 84.7 ± 11.4 kg) performed the Wingate test (WG) after ingestion of gelatin capsules containing either placebo (PL; dextrose) or caffeine (CAFF; 5 mg/kg body mass). The trials were separated by 1 week and subjects were asked to withdraw from all caffeine-containing products for 48 h before each trial. From the resulting power–time records, peak power (PP; highest power output in 5 s), minimum power (MP; lowest power output in 5 s), and the percent decline in power (Pd) were calculated. Surface EMG records of the right vastus lateralis (VL) and the gastrocnemius (GA) muscles corresponding to the PP and MP periods were collected and used to determine the integrated electromyogram (IEMG), the mean (MNPF), and the median (MDPF) of the signal’s power spectrum. A 2-way repeated measures analysis of variance (ANOVA) (treatment × time) was conducted to determine the effect of caffeine on these variables across levels of time. Caffeine ingestion had no effect on PP (PL, 1049 ± 192 W; CAFF, 1098 ± 198 W), MP (PL, 762 ± 104 W; CAFF, 802 ± 124 W), or the Pd (PL, 47% ± 8.9%; CAFF, 48.2% ± 7.3%) compared with the placebo. For both muscles, MNPF and MDPF diminished significantly (p < 0.001) across time and to a similar degree in both the CAFF and PL trials. Regardless of muscle, CAFF had no effect on the percent change in IEMG from the first 5 s to the last 5 s. For both treatments, the GA displayed a significantly (p < 0.05) greater pre vs. post percent decline in the EMG signal amplitude compared with the VL. These results indicate that caffeine does not impact power output during a 30 s high-intensity cycling bout. Furthermore, these data suggest that caffeine does not impact the neuromuscular drive as indicated by the similar IEMG scores between treatments. Similarly, caffeine does not seem to impact the frequency content of the surface EMG signal and thus the nature of recruited motor units before and after the expression of fatigue. The lack of decline in the IEMG in the VL despite the decline in power output over the course of the WG suggests a peripheral as opposed to a neural mechanism of fatigue in this muscle. The significant difference in the pre vs. post percent decline in the GA IEMG score further supports this notion. The pre vs. post decline in the IEMG noted in the GA may suggest a fatigue-triggered change in pedaling mechanics that may promote dominance of knee extensors with less reliance on plantar flexors.

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.

Pulse-shape distortion introduced by broadband deconvolution

1992 ◽  
Vol 82 (1) ◽  
pp. 238-258
Author(s):  
Stuart A. Sipkin ◽  
Arthur L. Lerner-Lam
Keyword(s):  
Transfer Function ◽  
Ground Motion ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequencies ◽  
Pass Filter ◽  
Long Period ◽  
High Pass Filter ◽  
Low Pass ◽  
High Pass

Abstract The availability of broadband digitally recorded seismic data has led to an increasing number of studies using data from which the instrument transfer function has been deconvolved. In most studies, it is assumed that raw ground motion is the quantity that remains after deconvolution. After deconvolving the instrument transfer function, however, seismograms are usually high-pass filtered to remove low-frequency noise caused by very long-period signals outside the frequency band of interest or instabilities in the instrument response at low frequencies. In some cases, data must also be low-pass filtered to remove high-frequency noise from various sources. Both of these operations are usually performed using either zero-phase (acausal) or minimum-phase (causal) filters. Use of these filters can lead to either bias or increased uncertainty in the results, especially when taking integral measures of the displacement pulse. We present a deconvolution method, based on Backus-Gilbert inverse theory, that regularizes the time-domain deconvolution problem and thus mitigates any low-frequency instabilities. We apply a roughening constraint that minimizes the long-period components of the deconvolved signal along with the misfit to the data, emphasizing the higher frequencies at the expense of low frequencies. Thus, the operator acts like a high-pass filter but is controlled by a trade-off parameter that depends on the ratio of the model variance to the residual variance, rather than an ad hoc selection of a filter corner frequency. The resulting deconvolved signal retains a higher fidelity to the original ground motion than that obtained using a postprocess high-pass filter and eliminates much of the bias introduced by such a filter. A smoothing operator can also be introduced that effectively applies a low-pass filter. This smoothing is useful in the presence of blue noise, or if inferences about source complexity are to be made from the roughness of the deconvolved signal.

scholarly journals Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 275
Author(s):  
Di Xu ◽  
Jianghua Ge ◽  
Yaping Wang ◽  
Junpeng Shao
Keyword(s):  
Stochastic Resonance ◽  
High Frequency ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Frequency Noise ◽  
Weak Signal ◽  
Resonance System ◽  
Weak Fault ◽  
High Pass ◽  
Fault Characteristic

In engineering practice, the bearing fault signal is composed of a series of complex multi-component signals containing multiple fault characteristics information. In the early stage of fault sprouting and evolution, the fault features are easily disturbed by noise and irrelevant signals, eliminating the fault signals in the strong background noise. To overcome the influence of noise on the signal, this study proposes multi-frequency weak signal decomposition and reconstruction of rolling bearing based on adaptive cascaded stochastic resonance. First, the original signal is passed through the Hilbert transform to obtain the envelope signal. The envelope signal is high-pass filtered to eliminate the interference of low-frequency components on the response of the stochastic resonance system. Secondly, cascaded stochastic resonance system parameters are adaptively optimized by the quantum particle swarm algorithm (QPSO). The high-pass filtered signal input to the adaptive cascaded stochastic resonance system (ACSRS) can further enhance the weak fault characteristics, allowing the gradual transfer of high-frequency noise energy to the low-frequency fault characteristic components. Finally, the signal is decomposed using the variational mode decomposition (VMD) method to jointly determine the location of the fault characteristic frequencies in the intrinsic mode functions (IMF) component by the energy loss coefficient and correlation coefficient to achieve the reconstruction of multi-frequency weak signals. Through simulation and experimental validation, the effectiveness and superiority of the method for multi-frequency weak signal detection in bearings are verified. The results show that the method not only achieves the adaptive optimization of the stochastic resonance system parameters gradually removing the high-frequency noise in the signal and improving the energy of the low-frequency signal but also reduces the number of decomposition layers of the VMD, enhances the fault characteristic information in the weak signal, and effectively identifies the early weak fault characteristics of rolling bearings.

Low-frequency noise attenuation in seismic and microseismic data using mathematical morphological filtering

2020 ◽  
Vol 222 (3) ◽  
pp. 1728-1749 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang ◽  
Shaohuan Zu ◽  
Yangkang Chen
Keyword(s):  
Frequency Band ◽  
Low Frequency ◽  
Frequency Noise ◽  
Noise Attenuation ◽  
Low Frequency Noise ◽  
Frequency Signal ◽  
Filtering Method ◽  
Morphological Filtering ◽  
High Pass ◽  
Filtering Approach

SUMMARY Low-frequency noise is one of the most common types of noise in seismic and microseismic data. Conventional approaches, such as the high-pass filtering method, utilize the low-frequency nature and distinguish between signal and noise based on their different frequency contents. However, conventional approaches are limited or even invalid when the signal and noise shares the same frequency band. Moreover, high-pass filtering method will suppress not only low-frequency noise but also low-frequency signal when they overlap in a same frequency band, which is extremely important in the inversion process for building the subsurface velocity model. To overcome the drawbacks of conventional high-pass filtering approach, we developed a novel method based on the mathematical morphology theorem to separate signal and noise using their differences in morphological scale. We extracted empirical relation between morphological scale and frequency band so that the mathematical morphology based approach can be conveniently used in low-frequency noise attenuation. The proposed method is termed as the mathematical morphological filtering (MMF) method. We compare the MMF approach with high-pass filtering and empirical mode decomposition (EMD) approaches using synthetic, reflection seismic and microseismic examples. The various examples demonstrate that the proposed MMF method can preserve more low-frequency signal than the high-pass filtering approach, and is more efficient and causes fewer artefacts than the EMD approach.

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