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。     

基于小波特征分析的手指动作识别研究

目的:本文利用表面肌电(sEMG)信号来研究多种手指组合动作的识别问题。方法:在对采集的四个通道sEMG信号进行降噪预处理的基础上,采用移动加窗处理方法来提取关于手指运动状态的信号活动段,再分析各个信号活动段的小波系数统计特征,进而利用多类支持向量机(SVM)分类算法来实现手指组合动作的识别。结果:动作识别率最高达到100%。结论:所采用方法能够有效地识别多种手势动作,并为后续基于肌电信号的实时人机接口系统的研究奠定了理论基础。     

基于小波特征分析的手指动作识别研究

目的：本文利用表面肌电（sEMG）信号来研究多种手指组合动作的识别问题。方法：在对采集的四个通道sEMG信号进行降噪预处理的基础上,采用移动加窗处理方法来提取关于手指运动状态的信号活动段,再分析各个信号活动段的小波系数统计特征,进而利用多类支持向量机（SVM分类算法来实现手指组合动作的识别。结果：动作识别率最高达到100％。结论：所采用方法能够有效地识别多种手势动作,并为后续基于肌电信号的实时人机接口系统的研究奠定了理论基础。     

基于重采样技术的肌电信号动作电位传导速度估计研究

目的:本文针对表面肌电(sEMG)信号探讨动作电位传导速度(APCV)估计问题。方法:以生理学仿真sEMG信号为基础,采用基于互相关分析的时延估计技术来获取相应的APCV估计值,并利用重采样技术来提高估计的精度。结果:实验表明,针对重采样后的仿真信号,其APCV的估计误差得到了明显降低。结论:所采用方法能够有效获取满意的APCV估计效果。     

基于小波变换的心电信号去噪算法

目的:去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信号,避免噪声对心电信号特征点的识别和提取造成误判和漏判。方法:首先利用coif4小波对心电信号按Mallat算法进行分解,然后采用软、硬阈值折衷与小波重构的算法进行去噪。结果:采用MIT/BIH Arrhythmia Database中的心电信号进行仿真、验证,有效去除了三种常见的噪声信号。结论:本方法实时性好,为临床分析与诊断奠定了基础。     

基于重采样技术的肌电信号动作电位传导速度估计研究

目的：本文针对表面肌电（sEMG）信号探讨动作电位传导速度（APCV）估计问题。方法：以生理学仿真sEMG信号为基础,采用基于互相关分析的时延估计技术来获取相应的APCV估计值,并利用重采样技术来提高估计的精度。结果：实验表明。针对重采样后的仿真信号,其APCV的估计误差得到了明显降低。结论：所采用方法能够有效获取满意的APCV估计效果。     

基于sEMG信号的操作者上肢肌肉施力疲劳评价模型研究

为评价操作者完成施力操作后上肢肌肉疲劳状态,提出了一种基于上肢肱二头肌的sEMG信号-疲劳程度主观感受的肌肉施力疲劳评价方法.为此,设置不同的拉/伸操作力,选取13名青年男性志愿者参加测试,记录其上肢肱二头肌操作至疲劳状态下的表面肌电信号sEMG;对于上肢肱二头肌局部疲劳的主观感受的评分,则采用通用的Borg量表来分级,由受试者完成操作后进行问卷而获得.然后,运用1/3倍频程分析方法对sEMG进行频域处理后,完成了sEMG-Borg分值的曲线拟合,得出上肢肌肉疲劳评价模型.根据数据分析结果,建立的二次曲线评价模型最为理想,它将为监测实际操作任务时人的疲劳状态并进行操作任务设计与分析提供依据与手段.     

心电信号的采集及R波同步检测

心电信号的R波识别在室速和房颤的同步除颤中具有十分重要的意义。传统的软件识别往往不能达到实时效果,而硬件识别则可以有效解决这一难题。本研究主要包括应用硬件电路构成的心电信号的采集系统和R波同步检测。硬件部分设计采用胸部双电极单导联输入心电信号,放大后再经微分电路和全波整流电路的处理,最后通过可选择的电压窗口比较器输出最终的R波信号检测结果。     

基于小波的心电信号有效性检测方法

目的：检测采集到的信号是否为有效心电信号,提高后续心电诊断和分析的准确率。方法：将采集到的信号进行预处理,即去噪处理,主要抑制基线漂移,50Hz工频及其谐波干扰和肌电干扰;取滑动窗长度为4s,检测该段内信号是否有效。为了验证算法的准确率及对不同心电波形是否具有普遍适用性,对MIT-BIH Arrhythmia Database中48个记录,CU及MIT-BIH Noise Stress Test Database中部分记录进行了仿真、验证。结果：仿真实验证明该方法能正确区分有效和无效信号,错检率较低,实现简单,适合实时处理。结论：本方法准确率高,能减少后续心电诊断和分析的计算量并提高准确率,特别是对室颤检测,符合心电分析的要求。     

表面肌电信号的下肢痉挛信号的特征分析与识别

脑卒中患者康复治疗中会引起下肢肌肉痉挛,这种现象给患者的康复训练过程带来极大的危害,因此能够在训练过程中识别痉挛并及时中断训练具有重要的实际意义。本研究通对下肢表面肌电信号的采集,采用基于形状的模版匹配法来识别痉挛信号,并以皮尔逊相关系数来分析表征下肢痉挛信号的相关性大小。分析结果表明,通过仿真验证了模版匹配法在个人痉挛信号识别中的准确性,显示了在泛用痉挛信号识别中的可行性。     

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.     

Tutorial. Surface EMG detection,conditioning and pre-processing: Best practices

This tutorial is aimed primarily to non-engineers, using or planning to use surface electromyography (sEMG) as an assessment tool for muscle evaluation in the prevention, monitoring, assessment and rehabilitation fields. The main purpose is to explain basic concepts related to: (a) signal detection (electrodes, electrode–skin interface, noise, ECG and power line interference), (b) basic signal properties, such as amplitude and bandwidth, (c) parameters of the front-end amplifier (input impedance, noise, CMRR, bandwidth, etc.), (d) techniques for interference and artifact reduction, (e) signal filtering, (f) sampling and (g) A/D conversion, These concepts are addressed and discussed, with examples.The second purpose is to outline best practices and provide general guidelines for proper signal detection, conditioning and A/D conversion, aimed to clinical operators and biomedical engineers. Issues related to the sEMG origin and to electrode size, interelectrode distance and location, have been discussed in a previous tutorial. Issues related to signal processing for information extraction will be discussed in a subsequent tutorial.     

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.     

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.     

The influence of digital filter type,amplitude normalisation method,and co-contraction algorithm on clinically relevant surface electromyography data during clinical movement assessments

There is a large and growing body of surface electromyography (sEMG) research using laboratory-specific signal processing procedures (i.e., digital filter type and amplitude normalisation protocols) and data analyses methods (i.e., co-contraction algorithms) to acquire practically meaningful information from these data. As a result, the ability to compare sEMG results between studies is, and continues to be challenging. The aim of this study was to determine if digital filter type, amplitude normalisation method, and co-contraction algorithm could influence the practical or clinical interpretation of processed sEMG data. Sixteen elite female athletes were recruited. During data collection, sEMG data was recorded from nine lower limb muscles while completing a series of calibration and clinical movement assessment trials (running and sidestepping). Three analyses were conducted: (1) signal processing with two different digital filter types (Butterworth or critically damped), (2) three amplitude normalisation methods, and (3) three co-contraction ratio algorithms. Results showed the choice of digital filter did not influence the clinical interpretation of sEMG; however, choice of amplitude normalisation method and co-contraction algorithm did influence the clinical interpretation of the running and sidestepping task. Care is recommended when choosing amplitude normalisation method and co-contraction algorithms if researchers/clinicians are interested in comparing sEMG data between studies.     

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.     

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.     

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.     

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.