基于运动神经元激励的表面肌电信号仿真研究

以单纤维动作电位的仿真为基础,结合运动单位的生理结构特点,利用神经肌肉系统激励与运动单位募集、发放间的近似关系,建立一个比较符合生理学特性的表面肌电(sEMG)信号模型,以仿真不同激励情况的sEMG信号.仿真实验发现,肌纤维与电极间距离的增加将使皮肤表面检测到的动作电位峰值下降;随着激励水平的提高,与仿真sEMG信号相关的收缩力逐渐增大,且仿真sEMG信号的时域波形以及频谱也与真实sEMG信号相似.实验结果表明仿真sEMG信号能够较有效地逼近真实sEMG信号,可用于运动单位发放检测等相关研究领域.     

食指力量对指浅屈肌运动单元募集模式的影响

目的检测不同食指力量水平下指浅屈肌运动单元的募集模式。方法设计食指20%、40%、60%最大随意收缩力量(maximum voluntary contraction,MVC)3个单指力量输出任务,采用8×1(行×列)阵列电极采集8名受试者的指浅屈肌sEMG信号,利用快速独立分量分析算法提取sEMG信号中运动单位动作电位(motor unit action potential,MUAP)信息,分析不同类型MUAP发放模式与力量的相关性。结果在原始信号中成功提取4种MUPA,且随力量水平的增加,MUAP总数目呈现递增趋势;不同力量水平下,4种类型MUAP所占比重不同,且随力量变化趋势不同。结论不同力量水平下,指浅屈肌改变运动单元募集模式以产生相应肌力。     

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short periodȁ9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.     

模型生理参数对表面肌电信号影响的仿真研究

通过模型研究肌肉生理参数对表面肌电信号的影响。根据肌肉的形态结构和生理特征,从肌电信号的信号源-细胞内动作电位开始,仿真了单肌纤维动作电位,由此合成了运动单位动作电位,再利用运动单位的募集发放模型,进一步仿真了运动单位动作电位序列,并最终完成了对表面肌电信号的仿真。在此基础上研究了极化区域宽度、跨膜电流密度分布和肌肉组织各向异性3个重要的模型生理参数对表面肌电信号统计特征的影响,得到了一些有价值的结果。实验结果表明,仿真肌电信号能够有效表征肌肉电生理变化过程。     

利用自组织竞争神经网络提取NEMG信号的MUAP模板

采用自组织竞争人工神经网络,完成对针电极肌电信号（NEMG）的运动单位动作电位（MUAP）的模式分类。MUAP波形的特征取自于其自回归（AR）模型系数a1～ap及激励白噪的功率ε^p构成的特征向量。模拟NEMG信号和真实NEMG信号的实验结果表明,这种分类方法具有很高的正确,从而为NEMG信号分解研究中提取MUAP模板提供了一条新的途径。     

Model for decomposition of the motor unit action potential 1 The algorithm

A model for decomposition of the motor unit action potential (MUAP) into its constituent single-fibre action potentials is presented. It finds an optimal fit of a set of simulated single-fibre action potentials (SSFAPs) to the MUAP. The SSFAPs are assumed to originate from muscle fibres at different distances from the electrode, having various delays in time. Two methods for decomposition of the MUAP are derived from this model: first, that the MUAP is decomposed into a fixed set of SSFAPs; and secondly that the MUAP is decomposed into an adaptive, expanding set of SSFAPs. In the second method three steps are used repeatedly. First, the MUAP is cross-correlated with a collection of four SSFAPs. Then the most similar SSFAPs are used to reconstruct the original MUAP. The reconstruction thus obtained is subtracted from the original MUAP to detect activity not yet imitated. This difference (‘residual’) is again used for cross-correlation, restarting in step 1. After a suitable number of iterations, the MUAP is optimally imitated by a set of SSFAPs. The set of SSFAPs, obtained as described, is assumed to give information about underlying anatomical and physiological data (such as fibre number, fibre density, impulse dispersion) of the motor unit under study.     

应用独立分量分析去除体表肌电中的心电干扰

体表肌电特别是从躯干获得的体表肌电往往受到被测对象自身心电信号的严重干扰。本文利用一种基于独立分量分析(ICA)的去噪方法,去除体表肌电中的心电干扰。该方法将多通道体表肌电进行独立分量分解,并用高通滤波器处理所分解出的心电独立分量以尽可能地保留其中的肌电成分,进而将去除心电干扰后的所有独立分量反向投影回原始信号空间得到去噪后的信号。仿真信号的处理结果表明,当高通滤波器的截止频率为30Hz时,该方法在有效去除心电干扰的同时使体表肌电的保真度达到最大。同时讨论了将信号的峰度(Kurtosis)值作为自动判别心电分量和肌电分量的标准的可能性。     

Model for decomposition of the motor unit action potential 2 Analysis of simulated motor unit action potentials

A model for decomposition of the motor unit action potential (MUAP) which finds an optimal fit of a set of simulated single-fibre action potentials (SSFAPs) to the original MUAP is tested. The composition of SSFAPs which best produces the MUAP is assumed to carry information about the actual distribution of single-fibre action potentials generating the MUAP. Two methods are derived from the model. The first makes use of a fixed set of SSFAPs. In the second method, a gradually expanding set of SSFAPs is built, using a sequence of crosscorrelation, optimal reconstruction and subtraction. In the paper MUAPs are constructed under various well defined conditions. The MUAPs are decomposed by the two methods, and the results are compared with traditional MUAP parameters. Under these conditions, the model obtains parameters with closer biological connections compared with traditional measures.     

New signal processing techniques for the decomposition of EMG signals

This paper relates to the use of knowledge-based signal processing techniques in the decomposition of EMG signals. The aim of the research is to automatically decompose EMG signals recorded at force levels up to 20 per cent maximum voluntary contraction (MVC) into their constitutent motor unit action potentials (MUAPS), and to display the MUAP shapes and firing times for the clinician. This requires the classification of nonoverlapping MUAPs and superimposed waveforms formed from overlapping MUAPs in the signal. Nonoverlapping MUAPs are classified using a statistical pattern-recognition method. The decomposition of superimposed waveforms uses a combination of procedural and knowledge-based methods. The decomposition method was tested on real and simulated EMG data recorded at force levels up to 20 per cent MVC. The different EMG signals contained up to six motor units (MUs). The new decomposition program classifies the total number of MUAP firings in an EMG signal with an accuracy always greater than 95 per cent. The decomposition program takes about 15 s to classify all nonoverlapping MUAPs in EMG signal of length 1·0 s and, on average, an extra 9s to classify each superimposed waveform.     

Adaptive spatial filtering of multichannel surface electromyogram signals

Spatial filtering of surface electromyography (EMG) signal can be used to enhance single motor unit action potentials (MUAPs). Traditional spatial filters for surface EMG do not take into consideration that some electrodes could have poor skin contact. In contrast to the traditional a priori defined filters, this study introduces an adaptive spatial filtering method that adapts to the signal characteristics. The adaptive filter, the maximum kurtosis filter (MKF), was obtained by using the linear combination of surrounding channels that maximises kurtosis. The MKF and conventional filters were applied to simulated EMG signals and to real EMG signals recorded with an electrode grid to evaluate their performance in detecting single motor units. The MKF was compared with conventional spatial filtering methods. Simulated signals, with different levels of spatially correlated noise, were used for comparison. The influence of one electrode with poor skin contact was also investigated. The MKF was found to be considerably better at enhancing a single MUAP than conventional methods for all levels of spatial correlation of the noise. For a spatial correlation of 0.97 of the noise, the improvement in the signal-to-noise ratio, where a MUAP could be detected, was at least 6 dB. With a simulated poor skin contact for one electrode, the improvement over the other methods was at least 19 dB.     

Decomposition and quantitative analysis of clinical electromyographic signals

Procedures for the quantitative analysis of clinical electromyographic (EMG) signals detected simultaneously using selective or micro and non-selective or macro electrodes are presented. The procedures first involve the decomposition of the micro signals and then the quantitative analysis of the resulting motor unit action potential trains (MUAPTs) in conjunction with the associated macro signal. The decomposition procedures consist of a series of algorithms that are successively and iteratively applied to resolve a composite micro EMG signal into its constituent MUAPTs. The algorithms involve the detection of motor unit action potentials (MUAPs), MUAP clustering and supervised classification and they use shape and firing pattern information along with data dependent assignment criteria to obtain robust performance across a variety of EMG signals. The accuracy, extent and speed with which a set of 10 representative 20-30 s, concentric needle detected, micro signals could be decomposed are reported and discussed. The decomposition algorithms had a maximum and average error rate of 2.5% and 0.7%, respectively, on average assigned 88.7% of the detected MUAPs and took between 4 to 8 s. Quantitative analysis techniques involving average micro and macro MUAP shapes, the variability of micro MUAPs shapes and motor unit firing patterns are described and results obtained from analysis of the data set used to evaluate the decomposition algorithms are summarized and discussed.     

基于卷积混合盲源分离技术的表面肌电信号分解研究

根据表面肌电信号（SEMG）形成的生理学特性，采用一种基于卷积混合过程的盲源分离技术来分析隐含在SEMG信号中的运动单位动作电位信息，利用仿真的SEMG信号对这种算法的分解性能进行实验研究，并与采用瞬时混合过程的独立分量分析（ICA）算法的分解性能进行比较，同时将该算法应用于真实SEMG信号的分解实验。研究结果表明，无论是对模拟SEMG信号还是真实SEMG信号，采用卷积混合盲源分离技术的分解方法均能得到较明显的分解效果，且该方法较符合表面肌电信号的形成过程，因而具有重要的研究价值。     

Experimental and modelling investigation of surface EMG spike analysis

A pattern classification method based on five measures extracted from the surface electromyographic (sEMG) signal is used to provide a unique characterization of the interference pattern for different motor unit behaviours. This study investigated the sensitivity of the five sEMG measures during the force gradation process. Tissue and electrode filtering effects were further evaluated using a sEMG model. Subjects (N=8) performed isometric elbow flexion contractions from 0 to 100% MVC. The sEMG signals from the biceps brachii were recorded simultaneously with force. The basic building block of the sEMG model was the detection of single fibre action potentials (SFAPs) through a homogeneous, equivalent isotropic, infinite volume conduction medium. The SFAPs were summed to generate single motor unit action potentials. The physiologic properties from a well-known muscle model and motor unit recruitment and firing rate schemes were combined to generate synthetic sEMG signals. The following pattern classification measures were calculated: mean spike amplitude, mean spike frequency, mean spike slope, mean spike duration, and the mean number of peaks per spike. Root-mean-square amplitude and mean power frequency were also calculated. Taken together, the experimental data and modelling analysis showed that below 50% MVC, the pattern classification measures were more sensitive to changes in force than traditional time and frequency measures. However, there are additional limitations associated with electrode distance from the source that must be explored further. Future experimental work should ensure that the inter-electrode distance is no greater than 1cm to mitigate the effects of tissue filtering.     

利用运动单位发放信息建立多通道信息的表面肌电信号分解

目的基于多通道信息的表面肌电(surfaceelectromyographic,sEMG)信号分解有助于弥补单通道分解时空间和发放信息不足的缺点.本文提出利用运动单位(motorunit,MU)的发放信息建立多通道 sEMG信号中属于同一 MU的模板映射关系,实现多导信号的信息互补,从而提高分解的准确率.方法对四导仿真信号先分别进行单通道分解,然后利用各通道之间的发放信息建立模板映射关系进行多通道分解.结果仿真实验结果显示单通道分解准确率平均为75%,多通道分解准确率为88%,表明利用MU发放信息建立模板映射关系进行sEMG信号分解能够提高分解有效性.结论将该方法应用于真实信号分解,也能有效得到 MU的波形和发放信息.     

基于HHT变换的SEMG信号中MUAP的数目估计

对表面肌电（SEMG）信号中单位动作电位（MUAP）的数目进行估计可为神经肌肉控制的理论研究和神经肌肉疾病的诊断开辟新途径,本文给出了一种基于Hilbert-Huang变换（HHT）的表面肌电信号中运动MUAP数目估计方法.通过对SEMG信号经验模态分解后的第一内禀模态函数分量进行瞬时频率分析,利用其瞬时频率极值点的计数即可估计出运动MUAP数目.仿真信号与真实信号的实验结果均表明,基于HHT的SEMG信号中MUAP的估计方法是有效的.     

MUAP Number Estimates in Surface EMG: Template-Matching Methods and Their Performance Boundaries

Estimates of the number of motor unit action potential (MUAP)s appearing in the surface electromyogram (EMG) signal, which offers potentially valuable information about motor unit recruitment and firing rates, are likely to provide a more accurate reflection of the neural command to muscle than are current EMG quantification methods. In this paper, we show that the basic shapes of surface MUAPs recorded from the first dorsal interosseous (FDI) muscle can ideally be represented by a small number of waveforms. On the basis of this, we seek to estimate the number of MUAPs present in standard surface EMG records, using template-matching techniques to identify MUAP occurrences. Our simulation study indicates that the performance of template-matching methods for MUAP number estimation is mainly constrained by the MUAP superposition in the signal, and the maximum number of MUAPs allowed in the signal for a good estimation is determined by the duration of MUAPs. To further explore this from experimental surface EMG signals, we compare the recordings from a selective multiple concentric ring electrode against those derived from a standard differential EMG electrode situated over the same muscle. We conclude that the ring surface electrode only slightly reduces the MUAP duration and the less MUAP superposition rate contained in the signal is mainly achieved by reducing the pick up area of the electrode. Using a template-matching method, although the number of MUAPs can be approximately estimated based on a very selective surface EMG recording at low force levels, the maximum number of MUAPs correctly estimated from the surface EMG is constrained by the MUAP duration.     

Median averaging of electromyographic motor unit action potentials: comparison with other techniques

A technique of extracting individual motor unit action potentials (MUAPs) from EMG signals by median averaging, a modification of an existing method, is presented. To compare different techniques of MUAP extraction, 89 MUAPs were recorded with a concentric needle electrode in the brachial biceps muscle of normal subjects and patients with nerve and muscle diseases. MUAPs were also extracted by another method, called split-sweep median averaging, in which alternate MUAP discharges are averaged independently in two computer buffers until the two averaged signals appear equal on visual inspection by the operator. The amplitude, area, area: amplitude ratio, duration and number of phases and turns of each extracted MUAP were determined by each technique. Overall, there was a strong correlation between all features of the MUAPs extracted by median and splitsweep averaging, although the latter method required, on average, twice as many MUAP discharges to produce acceptable signals. We thus conclude that median averaging is a fast and accurate method that requires relatively few MU discharges to extract MUAP signals from spurious background signals.     

Robust supervised classification of motor unit action potentials

A certainty-based classification algorithm is described, which comprises part of a clinically used EMG signal decomposition system. This algorithm classifies a candidate motor unit action potential (MUAP) to the motor unit action potential train (MUAPT) that produces the greatest estimated certainty, provided this maximal certainty is above a given threshold. The algorithm is iterative, such that the certainty with which assignments are made increases with each pass through the data, and it has specific stopping criteria. The performance and sensitivity (to the assignment threshold) of the Certainty algorithm and an iterative minimum Euclidean distance (MED) algorithm are compared by classifying sets of MUAPs detected in real concentric needle-detected EMG signals, using a range of assignment thresholds for each algorithm. With regard to MUAP assignment and error rates, the Certainty algorithm consistently provides better mean results and, more importantly, less variable results than the MED algorithm. The Certainty algorithm can provide mean assignment and error rates of 80.8 and 1.5%, respectively, with a maximum error rate of 3.2%; the MED algorithm can provide mean assignment and error rates of 80.3 and 3.3%, respectively, with a maximum error rate of 6.5%. The Certainty algorithm is relatively insensitive to the certainty threshold used, can consistently differentiate between similarly shaped MUAPs from different MUAPTs, and can make correct classifications despite biological shape variability, background noise and signal shape non-stationarity.     

A new and fast approach towards sEMG decomposition

The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of present motor unit action potentials (MUAPs). Then, using shapes detected on these channels, the hierarchical clustering algorithm as reported by Quian Quiroga et al. (Neural Comput 16:1661–1687, 2004) is extended for multichannel data in order to obtain the motor unit action potential (MUAP) signatures. After this first step, more motor unit firings are obtained using the extracted signatures by a novel demixing technique. In this demixing stage, we propose a time-efficient solution for the general convolutive system that models the motor unit firings on the HD-sEMG grid. We constrain this system by using the extracted signatures as prior knowledge and reconstruct the firing patterns in a computationally efficient way. The algorithm performance is successfully verified on simulated data containing up to 20 different MUAP signatures. Moreover, we tested the method on real low contraction recordings from the lateral vastus leg muscle by comparing the algorithm’s output to the results obtained by manual analysis of the data from two independent trained operators. The proposed method showed to perform about equally successful as the operators.     

A novel method for automated EMG decomposition and MUAP classification

OBJECTIVE: This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. METHODOLOGY: The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. RESULTS: The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. CONCLUSION: The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals.