表面肌电信号采集与降噪处理

目的:本文以设计的表面肌电(sEMG)信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法:结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果:通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论:所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

表面肌电信号采集与降噪处理

目的：本文以设计的表面~g（sEMG）信号采集系统为基础,探讨sEMG信号中的降噪处理问题。方法：结合sEMG信号的噪声影响情况,首先利用带通滤波器消除肌电信号频带外噪声,再通过频谱插值法来抑制工频干扰分量,最后使用小波分析方法来削弱肌电信号频带内噪声。结果：通过对检测sEMG信号的降噪处理,信号噪声得到明显抑制。结论：所设计采集系统能够获得满意的sEMG信号检测效果,所采用降噪方法能够有效提高sEMG信号的质量。     

Reliability of surface EMG matrix in locating the innervation zone of upper trapezius muscle

The identification of the motor unit (MU) innervation zone (IZ) using surface electromyographic (sEMG) signals detected on the skin with a linear array or a matrix of electrodes has been recently proposed in the literature. However, an analysis of the reliability of this procedure and, therefore, of the suitability of the sEMG signals for this purpose has not been reported.The purpose of this work is to describe the intra and inter-rater reliability and the suitability of surface EMG in locating the innervation zone of the upper trapezius muscle.Two operators were trained on electrode matrix positioning and sEMG signal analysis. Ten healthy subjects, instructed to perform a series of isometric contractions of the upper trapezius muscle participated in the study. The two operators collected sEMG signals and then independently estimated the IZ location through visual analysis.Results showed an almost perfect agreement for intra-rater and inter-rater reliability. The constancy of IZ location could be affected by the factors reflecting the population of active MUs and their IZs, including: the contraction intensity, the acquisition period analyzed, the contraction repetition. In almost all cases the IZ location shift due to these factors did not exceed 4 mm. Results generalization to other muscles should be made with caution.     

Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis

The visual inspection is a widely used method for evaluating the surface electromyographic signal (sEMG) during deglutition, a process highly dependent of the examiners expertise. It is desirable to have a less subjective and automated technique to improve the onset detection in swallowing related muscles, which have a low signal-to-noise ratio. In this work, we acquired sEMG measured in infrahyoid muscles with high baseline noise of ten healthy adults during water swallowing tasks. Two methods were applied to find the combination of cutoff frequencies that achieve the most accurate onset detection: discrete wavelet decomposition based method and fixed steps variations of low and high cutoff frequencies of a digital bandpass filter. Teager-Kaiser Energy operator, root mean square and simple threshold method were applied for both techniques. Results show a narrowing of the effective bandwidth vs. the literature recommended parameters for sEMG acquisition. Both level 3 decomposition with mother wavelet db4 and bandpass filter with cutoff frequencies between 130 and 180 Hz were optimal for onset detection in infrahyoid muscles. The proposed methodologies recognized the onset time with predictive power above 0.95, that is similar to previous findings but in larger and more superficial muscles in limbs.     

Filtering the surface EMG signal: Movement artifact and baseline noise contamination

The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources.     

A thin, flexible multielectrode grid for high-density surface EMG.

Although the value of high-density surface electromyography (sEMG) has already been proven in fundamental research and for specific diagnostic questions, there is as yet no broad clinical application. This is partly due to limitations of construction principles and application techniques of conventional electrode array systems. We developed a thin, highly flexible, two-dimensional multielectrode sEMG grid, which is manufactured by using flexprint techniques. The material used as electrode carrier (Polyimid, 50 microm thick) allows grids to be cut out in any required shape or size. One universal grid version can therefore be used for many applications, thereby reducing costs. The reusable electrode grid is attached to the skin by using specially prepared double-sided adhesive tape, which allows the selective application of conductive cream only directly below the detection surfaces. To explore the practical possibilities, this technique was applied in single motor unit analysis of the facial musculature. The high mechanical flexibility allowed the electrode grid to follow the skin surface even in areas with very uneven contours, resulting in good electrical connections in the whole recording area. The silverchloride surfaces of the electrodes and their low electrode-to-skin impedances guaranteed high baseline stability and a low signal noise level. The electrode-to-skin attachment proved to withstand saliva and great tensile forces due to mimic contractions. The inexpensive, universally adaptable and minimally obstructive sensor allows the principal advantages of high-density sEMG to be extended to all skeletal muscles accessible from the skin surface and may lay the foundation for more broad clinical application of this noninvasive, two-dimensional sEMG technique.     

局部肌肉疲劳的表面肌电信号复杂度和熵变化

目的 在于探讨静态和动态疲劳性运动过程中肱二头肌和腰部脊竖肌表面肌电(surface electromyography,sEMG)信号的Lempel-Ziv复杂度和Kolmogorov熵的变化规律。18名男性大学生志愿者被随机分为肱二头肌和腰部脊竖肌运动负荷组,分别完成静态和动态疲劳运动负荷试验。运动负荷期间连续记录sEMG信号,在对运动负荷时间和重复次数进行标准化处理后,截取相应时段的sEMG信号,计算Lempel-Ziv复杂度和Kolmogorov熵,观察它们随肌肉疲劳发展的变化规律。研究结果表明,无论是静态还是动态疲劳运动条件下,被检肌肉sEMG信号的复杂度和熵均随着运动负荷时间呈现明显的单调递减型变化。该变化可能与神经系统渐进性协调众多运动单位同步收缩的‘协同效应”有关。     

High frequency vibration conditioning stimulation centrally reduces myoelectrical manifestation of fatigue in healthy subjects

ObjectiveVibration conditioning has been adopted as a tool to improve muscle force and reduce fatigue onset in various rehabilitation settings. This study was designed to asses if high frequency vibration can induce some conditioning effects detectable in surface EMG (sEMG) signal; and whether these effects are central or peripheral in origin.Design300 Hz vibration was applied for 30 min during 5 consecutive days, to the right biceps brachii muscle of 10 healthy males aged from 25 to 50 years. sEMG was recorded with a 16 electrode linear array placed on the skin overlying the vibrated muscle. The test protocol consisted of 30% and 60% maximal voluntary contraction (MVC) as well as involuntary (electrically elicited) contractions before and after treatment.ResultsNo statistically significant differences were found between PRE and POST vibration conditioning when involuntary stimulus-evoked contraction and 30% MVC were used. Significant differences in the initial values and rates of change of muscle fibre conduction velocity were found only at 60% MVC.Conclusions300 Hz vibration did not induce any peripheral changes as demonstrated by the lack of differences when fatigue was electrically induced. Differences were found only when the muscle was voluntarily fatigued at 60% MVC suggesting a modification in the centrally driven motor unit recruitment order, and interpreted as an adaptive response to the reiteration of the vibratory conditioning.     

Use of surface electromyography to estimate neck muscle activity

This paper reviews the literature concerning the use of surface electromyography (sEMG) for the study of the neck musculature in response to work and workplace design during light work and semi-static tasks. The paper also draws upon basic research and biomechanical modeling in order to provide methodological recommendations for the use of surface electromyography in this region of the body and to identify areas which require further investigation. The paper includes review and discussion of electrode site location, methods of normalization, data reliability, and factors that can affect sEMG signals from this region, including noise, physiologic artifact, stress, visual deficiencies, and pain. General guidance for maximum exertions with the neck musculature, for sEMG normalization or other purposes, is also included.     

Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals

ObjectivesElectromyography (EMG) is recording of the electrical activity produced by skeletal muscles. The classification of the EMG signals for different physical actions can be useful in restoring some or all of the lost motor functionalities in these individuals. Accuracy in classifying the EMG signal indicates efficient control of prosthesis.Material and methodsThe flexible analytic wavelet transform (FAWT) is used for classification of surface electromyography (sEMG) signals for identification of physical actions. FAWT is an efficient method for decomposition of sEMG signal into eight sub-bands, features namely neg-entropy, mean absolute value (MAV), variance (VAR), modified mean absolute value type 1 (MAV1), waveform length (WL), simple square integral (SSI), Tsallis entropy, integrated EMG (IEMG) are extracted from the sub-bands. Extracted features are fed into an extreme learning machine (ELM) classifier with sigmoid activation function.ResultsComprehensive experiments are conducted on the input sEMG signals and the accuracy, sensitivity and specificity scores are used for performance measurement. Experiments showed that among all sub-bands, the seventh sub-band provided the best performance where the recorded accuracy, sensitivity and specificity values were 99.36%, 99.36% and 99.93%, respectively. The comparison results showed best efficiency of proposed method as compared to other methods on the same dataset.ConclusionThis paper investigates the usage of the FAWT and ELM on sEMG signal classification. The results show that the proposed method is quite efficient in classification of the sEMG signals. It is also observed that the seventh sub-band of the FAWT provides the best discrimination property. In the future works, recent wavelet transform methods will be used for improving the classification performance.     

A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties

Introduction

Surface electromyography (sEMG) is the measurement of the electrical activity of the skeletal muscle tissue detected at the skin’s surface. Typically, a bipolar electrode configuration is used. Most muscles have pennate and/or curved fibres, meaning it is not always feasible to align the bipolar electrodes along the fibres direction. Hence, there is a need to explore how different electrode designs can affect sEMG measurements.

Method

A three layer finite element (skin, fat, muscle) muscle model was used to explore different electrode designs. The implemented model used as source signal an experimentally recorded intramuscular EMG taken from the biceps brachii muscle of one healthy male. A wavelet based intensity analysis of the simulated sEMG signal was performed to analyze the power of the signal in the time and frequency domain.

Results

The model showed muscle tissue causing a bandwidth reduction (to 20-92- Hz). The inter-electrode distance (IED) and the electrode orientation relative to the fibres affected the total power but not the frequency filtering response. The effect of significant misalignment between the electrodes and the fibres (60°- 90°) could be reduced by increasing the IED (25–30 mm), which attenuates signal cancellation. When modelling pennated fibres, the muscle tissue started to act as a low pass filter. The effect of different IED seems to be enhanced in the pennated model, while the filtering response is changed considerably only when the electrodes are close to the signal termination within the model. For pennation angle greater than 20°, more than 50% of the source signal was attenuated, which can be compensated by increasing the IED to 25 mm.

Conclusion

Differences in tissue filtering properties, shown in our model, indicates that different electrode designs should be considered for muscle with different geometric properties (i.e. pennated muscles).     

The effect of perspiration on the sEMG amplitude and power spectrum

Sweat accumulation underneath surface EMG (sEMG) electrodes is a common problem in workplace studies which compromises electrode adherence to the skin as well as signal fidelity. In this study, the effect of sweat accumulation on signal amplitude and mean frequency (MF) was examined to determine if the sEMG signal becomes altered through the sweat layer and whether this effect can be avoided by interrupting the pool of sweat using a thin strip of medical adhesive between the electrode snaps. Nine males performed a maximum, isometric contraction of their right quadriceps as sEMG was collected. Skin conditions under the electrode were dry and wet in incremental layers of 0.02 mm of artificial sweat. The results demonstrated that sweat accumulation under sEMG electrodes dampens the amplitude of the EMG signal in a predictable way (r = .88 and .97 for double and single snap electrodes, respectively) with almost 2% and 3% deterioration for every 0.02 mm of sweat depending on the type of electrode used. The medical adhesive proved to be highly effective at preventing amplitude deterioration indicating that signal shunting can be prevented. MF was not influenced by sweat accumulation even under the extreme wet condition.     

慢性非特异性颈部不适飞行员与无症状飞行员之间头颈夹肌表面肌电特征的比较

目的:通过对慢性非特异性颈部不适飞行员与无症状飞行员之间头颈夹肌表面肌电特征的比较,为慢性非特异性颈部不适飞行员的早期诊断提供参考依据。方法:用表面肌电仪测量52名慢性非特异性颈部不适飞行员与11名无症状飞行员双侧头颈夹肌的松弛状态、等长收缩以及异长收缩时的表面肌电特征,分析不同状态下双侧头颈夹肌疲劳性肌电指标MFs、MPFs、ZCRs以及平均肌电AEMG值,用统计学软件SPSS 18.0分析实验结果。结果:非特异性颈部不适飞行员与无症状飞行员双侧头颈夹肌在松弛状态时疲劳性肌电指标MFs、MPFs、ZCRs比较均无统计学差异(P0.05),其双侧头颈夹肌在等长收缩和异长收缩时间有统计学差异(P0.05)。头颈夹肌处于松弛状态时,两组间平均肌电AEMG比较无统计学差异(P0.05),而在肌肉等长收缩和异长收缩时,两组间比较有统计学差异(P0.05)。结论:慢性非特异性颈部不适飞行员与无症状飞行员之间头颈夹肌表面肌电相关指标存在统计学差异,慢性颈部不适的飞行员相比无症状飞行员更易出现头颈夹肌的疲劳以及头颈夹肌的功能下降。头颈夹肌表面肌电特征有助于早期诊断慢性非特异性颈部不适飞行员的肌肉功能状态的改变。     

Comparison of surface electromyographic (sEMG) activity of submental muscles between the head lift and tongue press exercises as a therapeutic exercise for pharyngeal dysphagia

Objective: The present study compared surface electromyographic (sEMG) activity obtained from the submental muscle group for a tongue press and a head lift exercise as potential therapeutic exercises for dysphagic elderly. Materials and methods: Fifty‐three healthy volunteers with a mean age of 35.3 participated in this study. Subjects were required to perform an isometric task, pressing their tongue against the hard palate, and an isotonic task requiring sustained lingual force against the hard palate. Pressure sensors were used to measure the amount of lingual pressure against the hard palate. Submental sEMG data from these tasks were compared with those obtained from the isometric and isotonic aspects of a head lift exercise. Results: No sEMG differences were identified between the isometric tongue press task and head lift exercise. Isotonic tongue press exercises resulted in significantly higher maximum and mean sEMG values compared with the isotonic head lift exercise (p < 0.05). The submental sEMG activity from the tongue press exercise was equal (isometric) to, or greater (isotonic) than comparable muscle activation obtained during the head lift exercise. Conclusions: The tongue press exercise may be less strenuous than the head lift exercise while achieving the same therapeutic effect.     

静态负荷诱发肌肉疲劳后恢复期sEMG信号变化规律

目的:探讨肌肉疲劳过程中sEMG功率谱变化与H 的关系以及可能存在的其它影响因素.方法:利用肌肉进行疲劳收缩结束后,短时间内肌肉pH值尚无明显改变的特性,观察恢复期30 s内s EMG功率谱的变化规律.八名男性受试者,以肱二头肌为目标肌肉,负荷强度为60%MVC,静态持续负荷至疲劳点后,在恢复期以同样负荷分别观察2 s、4 s、6 s、8 s、10 s、20 s、30 s时的sEMG信号特征.结果:肱二头肌在以60%MVC静态疲劳负荷过程中MPF呈线性下降.在疲劳负荷后的恢复期,MPF恢复极其迅速,运动结束后仅2 s,MPF已恢复到整个下降范围的26.5%;至30 s,MPF已恢复到整个下降范围的87.7%.结论:由[H ]增加引起的肌纤维动作电位传导速度下降不是决定sEMG功率谱左移的唯一因素,提示sEMG功率谱左移可能与神经源性的中枢机制的作用有关.     

Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments

The purpose of this paper was to evaluate the effects of the longitudinal single differential (LSD), the longitudinal double differential (LDD) and the normal double differential (NDD) spatial filters, the electrode shape, the inter-electrode distance (IED) on non-Gaussianity and non-linearity levels of simulated surface EMG (sEMG) signals when the maximum voluntary contraction (MVC) varied from 10% to 100% by a step of 10%. The effects of recruitment range thresholds (RR), the firing rate (FR) strategy and the peak firing rate (PFR) of motor units were also considered.A cylindrical multilayer model of the volume conductor and a model of motor unit (MU) recruitment and firing rate were used to simulate sEMG signals in a pool of 120 MUs for 5 s. Firstly, the stationarity of sEMG signals was tested by the runs, the reverse arrangements (RA) and the modified reverse arrangements (MRA) tests. Then the non-Gaussianity was characterised with bicoherence and kurtosis, and non-linearity levels was evaluated with linearity test.The kurtosis analysis showed that the sEMG signals detected by the LSD filter were the most Gaussian and those detected by the NDD filter were the least Gaussian. In addition, the sEMG signals detected by the LSD filter were the most linear. For a given filter, the sEMG signals detected by using rectangular electrodes were more Gaussian and more linear than that detected with circular electrodes. Moreover, the sEMG signals are less non-Gaussian and more linear with reverse onion-skin firing rate strategy than those with onion-skin strategy. The levels of sEMG signal Gaussianity and linearity increased with the increase of the IED, RR and PFR.     

Audio-visual materials for genetics

This paper describes the use of hypermedia technology in an innovative manner to produce a tutorial on ceil ultrastructure for first-year undergraduate students with little or no experience of formal biology tuition. This replaced human tutorial support but was used in conjunction with traditional lecture and practical teaching. The study aimed to ascertain if there was any enhancement of the students' learning experience. Whilst interviews with students revealed that the tutorial had been stimulating and worthwhile, a multiple-choice assessment test used to compare these students' performances with those of students who received a traditional form of tutorial indicated that the computer-mediated tutorial did not provide any enhancement to learning. The findings raise questions regarding the use of multiple-choice methods of assessment which are discussed in terms of implications for student-centred learning strategies.     

Effect of adaptive motion-artifact reduction on QRS detection

Motion artifact resulting from electrode and patient movement is a significant source of noise in ECG, EEG, EMG, and impedance pneumography recording. Noise resulting from motion is particularly troublesome in ambulatory ECG recordings, such as those made during Holter monitoring or stress tests, because the bandwidth of the motion artifact overlaps with the ECG signal bandwidth. The authors investigated the effect of an adaptive motion-artifact removal algorithm on the performance of a standard QRS detector. They made four ECG recordings on each of the three subjects while manually generating artifact. Adaptive noise removal was applied to the ECG signal using a skin-stretch signal as the noise reference. Adaptive noise removal reduced the number of false QRS detections in the records from 380 to 104, for an average reduction in false detections of 72.6%. False-detection reductions for individual records ranged from 12% to 93%.     

Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates.

It is generally assumed that raw surface EMG (sEMG) should be high pass filtered with cutoffs of 10-30 Hz to remove motion artifact before subsequent processing to estimate muscle force. The purpose of the current study was to explore the benefits of filtering out much of the raw sEMG signal when attempting to estimate accurate muscle forces. Twenty-five subjects were studied as they performed rapid static, anisotonic contractions of the biceps brachii. Biceps force was estimated (as a percentage of maximum) based on forces recorded at the wrist. An iterative approach was used to process the sEMG from the biceps brachii, using progressively greater high pass cutoff frequencies (20-440 Hz in steps of 30 Hz) with first and sixth order filters, as well as signal whitening, to determine the effects on the accuracy of EMG-based biceps force estimates. The results indicate that removing up to 99% of the raw sEMG signal power resulted in significant and substantial improvements in biceps force estimates. These findings challenge previous assumptions that the raw sEMG signal power between about 20 and 500 Hz should used when estimating muscle force. For the purposes of force prediction, it appears that a much smaller, high band of sEMG frequencies may be associated with force and the remainder of the spectrum has little relevance for force estimation.     

sEMG wavelet-based indices predicts muscle power loss during dynamic contractions

The purpose of this study was to investigate the sensitivity of new surface electromyography (sEMG) indices based on the discrete wavelet transform to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg press, with 2 min rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean rectified voltage (MRV), median spectral frequency (F_med), Dimitrov spectral index of muscle fatigue (FI_nsm5), as well as five other parameters obtained from the stationary wavelet transform (SWT) as ratios between different scales. The new wavelet indices showed better accuracy to map changes in muscle power output during the fatiguing protocol. Moreover, the new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log-FI_nsm5 and MRV as a two-factor combination predictor accounted for 49.8%. On the other hand, the new wavelet indices proposed, showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task.