基于时频分析检测EEG中癫痫样棘/尖波的方法

提出了一种基于Ｃｈｏｉ－Ｗｉｌｌｉａｍｓ分布检测ＥＥＧ中癫痫样棘波／尖波的方法。该方法通过计算ＥＥＧ信号的时频分布,得到一段信号在各个时刻上沿频率方向上的能量分布。这种能量分布相当于一种瞬时频谱,反映了ＥＥＧ信号在局部时间范围里的波形特征。以一段ＥＥＧ信号在各个时刻的瞬时频谱的平均作为这段脑电的背景信号频谱,通过计算每一时刻的瞬时频谱与背景信号频谱之间的频谱差,检测这段信号中的棘波／尖波。对临床Ｅ     

等长伸膝动作的运动单元放电特征分析

本研究基于表面肌电分解技术,分析伸膝动作中不同发力状态下大腿肌肉运动单元的解码准确性,并对比神经特征和肌电特征在肌肉激活程度估计中的效果. 12名大学生分别以2种发力速度和4种发力等级完成伸膝动作的等长收缩.实验同步采集受试者股内侧肌和股外侧肌处的高密度表面肌电信号和伸膝动作收缩力.基于卷积核补偿算法解码肌电信号得到运动单元动作电位,提取神经特征用于收缩力的互相关分析.结果发现,对于股内侧肌,2种任务及4种收缩力等级下平均解码得到(7±4)个运动单元,股外侧肌平均解码得到(9±5)个运动单元.它们的平均脉冲信噪比(pulse-to-noise ratio,PNR)为30.1 d B,对应解码准确率大于90%.股内侧肌的两种神经特征与力之间的平均相关性分别为(0.79±0.08)和(0.80±0.08),股外侧肌的两种神经特征与力之间的平均相关性分别为(0.85±0.05)和(0.85±0.06).综上可见,基于肌电分解技术可以准确识别不同发力状态下大腿肌肉的运动单元放电活动,并且运动单元放电频率与伸膝动作力高度相关,研究结果可用于运动康复、运动训练及人机接口等领域.     

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。     

不同负荷方式引起的腰部肌肉表面肌电信号变化特征

采用时频、复杂度和定量递归信号分析方法对Biering-sorensen和Ito-Shirado条件下腰部肌肉表面肌电信号的变化规律和特点进行了比较。10名正常受试者分别参加Biering-sorensen和Ito-Shirado运动负荷试验,分别获取两侧L2～L3和L5～S1部位表面肌电信号。研究发现,以上两种运动负荷方式下平均功率频率和复杂度时间序列曲线呈单调递减型变化,而确定性线段百分数时间序列曲线呈单调递增型变化。双因素方差分析表明负荷方式和采样部位对以上信号分析指标变化斜率的影响均有显著统计学意义,证明Biering-sorensen负荷方式较Ito-Shirado负荷方式对腰部肌肉表面肌电信号特征有较大的影响,而采样部位也是腰部肌肉功能评价不可忽视的因素。     

基于定量分析方法的动作表面肌电信号分析

介绍了非线性数据处理方法递归图法(recurrence plots,RP)及其定量分析方法(recurrence quantifi-cation analysis,RQA),并利用RP和RQA研究了动作表面肌电信号。研究发现,表面肌电信号在不同动作模式下其所对应的RP图在结构上差异明显,通过计算两通道肌电信号的RQA指标递归率,发现不同动作信号的RQA指标递归率值具有不同的聚类分布。该方法为肌电信号的动作模式分类提供了一种新的思路。     

基于小波变换的单次诱发电位信号时频分析

比较小波变换和平均叠加两种方法提取“模拟自然阅读”刺激模式下的诱发脑电信号,分析其时频特性,并进行脑功能源分布定位分析。结果显示,采用平均叠加法来提取和分析诱发电位信号,损失了某些重要的诱发电位成分,且其功能源分布定位反映的只是等效功能源的静态过程;而使用小波变换和脑功能源定位来提取和分析单次诱发电位信号,既能观察到丰富的诱发电位成分,又能反映脑功能源的实时动态活动过程。这表明,小波变换下的时频分析是脑电信号处理的一种可行的新方法。     

等长收缩诱发肌肉疲劳及恢复过程中表面肌电信号特征变化规律

运用线性和非线性分析方法分析不同强度等长收缩诱发局部肌肉疲劳及恢复过程中表面肌电信号（surface electromyogram,sEMG）特征的变化规律,探讨影响sEMG信号变化的可能原因和机制.结果显示,在肱二头肌疲劳收缩过程中,sEMG的特征指标平均肌电值（average EMG,AEMG）、平均功率频率（mean power frequency,MPF）、Lempel-Ziv复杂度（Lempel-Ziv complexity,C（n））和确定性线段百分数（Determinism%,% DET）的变化具有良好的规律性.恢复期AEMG没有表现出规律性的变化,MPF、C（n）和?T在恢复期2秒即开始显著恢复,在前10秒恢复很快,随后恢复速度变慢.恢复初期sEMG信号特征的快速变化提示中枢控制因素可能发挥更大作用.     

不同姿势久坐下女性腰部肌肉的sEMG信号变化

目的:观察女性受试者在不同坐姿久坐前后腰部肌肉表面肌电(sEMG)信号的变化,探讨不同姿势的久坐对竖脊肌功能状态的影响。方法:32名女性受试者随机分成4组,分别在4种不同的座椅(座椅A、B、C、D)上久坐2 h。记录受试者腰部竖脊肌在久坐前后2次最大随意收缩力量(MVC)测试中的sEMG信号,观察测试过程中的前3 s时频指标及全程频域指标的变化。结果:3 s的时频指标平均肌电振幅(AEMG)、平均功率谱频率(MPF)在不同坐姿久坐前后无显著性差异,其中AEMG在座椅B组中明显大于座椅A组;全程信号的频域指标MPF在久坐后显著减小,但在不同坐姿之间无显著性差异。结论:女性受试者在4种不同坐姿2 h久坐前后腰部竖脊肌的最大活动水平无明显改变;最大持续收缩能力在久坐后下降,但在不同坐姿间并无显著差异。     

肌电信号的特征分析方法及其应用

肌电信号是产生肌力的生物电信号来源,反映了神经-肌肉系统在进行随意性和非随意性活动时的生物电变化情况,它与神经肌肉活动密切相关.伴随着肌电信号特征分析方法的日臻完善,蕴含在信号内的神经、肌肉信息,越来越多地被人们所掌握,并被广泛地应用于临床医学、康复医学、体育科学、医学工程学以及基础研究等诸多领域.因而肌电信号具有重要的应用价值和学术价值.现本文主要针对肌电信号的特征分析方法(时域分析、频谱分析、时频分析等方法)以及肌电信号相关领域的应用情况作以综述.     

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。     

Time-frequency methods applied to muscle fatigue assessment during dynamic contractions.

This paper discusses the assessment of the electrical manifestations of muscle fatigue during dynamic contractions. In the past, the study of muscle fatigue was restricted to isometric constant force contractions because, in this contraction paradigm, the myoelectric signal may be considered as wide sense stationary over epochs lasting up to two or three seconds, and hence classic spectral estimation techniques may be applied. Recently, the availability of spectral estimation techniques specifically designed for nonstationary signal analysis made it possible to extend the employment of muscle fatigue assessment to cyclic dynamic contractions, thus increasing noticeably its possible clinical applications. After presenting the basics of time-frequency distributions, we introduce instantaneous spectral parameters well suited to tracking spectral changes due to muscle fatigue, discuss the issues of quasi-stationarity and quasi-cyclostationarity, and present different strategies of signal analysis to be utilized with cyclic dynamic contractions. We present preliminary results obtained by analyzing data collected from paraspinal muscles during repetitive lift movements, from the first dorsal interosseus during abduction-adduction movements of the index finger, and from knee flexors and extensors during isokinetic exercise. In conclusion, data herein reported demonstrate that the described techniques allow for evidencing the electrical manifestations of muscle fatigue in different paradigms of cyclic dynamic contractions. We believe that the extension of the objective assessment of the electrical manifestations of muscle fatigue from static to dynamic contractions may increase considerably the interest of researchers and clinicians and open new application fields, as ergonomics and sports medicine.     

Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions

The time course of muscle fiber conduction velocity and surface myoelectric signal spectral (mean and median frequency of the power spectrum) and amplitude (average rectified and root-mean-square value) parameters was studied in 20 experiments on the tibialis anterior muscle of 10 healthy human subjects during sustained isometric voluntary or electrically elicited contractions. Voluntary contractions at 20% maximal voluntary contraction (MVC) and at 80% MVC with duration of 20 s were performed at the beginning of each experiment. Tetanic electrical stimulation was then applied to the main muscle motor point for 20 s with surface electrodes at five stimulation frequencies (20, 25, 30, 35, and 40 Hz). All subjects showed myoelectric manifestations of muscle fatigue consisting of negative trends of spectral variables and conduction velocity and positive trends of amplitude variables. The main findings of this work are 1) myoelectric signal variables obtained from electrically elicited contractions show fluctuations smaller than those observed in voluntary contractions, 2) spectral variables are more sensitive to fatigue than conduction velocity and the average rectified value is more sensitive to fatigue than the root-mean-square value, 3) conduction velocity is not the only physiological factor affecting spectral variables, and 4) contractions elicited at supramaximal stimulation and frequencies greater than 30 Hz demonstrate myoelectric manifestations of muscle fatigue greater than those observed at 80% MVC sustained for the same time.     

An estimation of the influence of force decrease on the mean power spectral frequency shift of the EMG during repetitive maximum dynamic knee extensions.

Frequency analysis of myoelectric (ME) signals, using the mean power spectral frequency (MNF), has been widely used to characterize peripheral muscle fatigue during isometric contractions assuming constant force. However, during repetitive isokinetic contractions performed with maximum effort, output (force or torque) will decrease markedly during the initial 40-60 contractions, followed by a phase with little or no change. MNF shows a similar pattern. In situations where there exist a significant relationship between MNF and output, part of the decrease in MNF may per se be related to the decrease in force during dynamic contractions. This study estimated force effects on the MNF shifts during repetitive dynamic knee extensions. Twenty healthy volunteers participated in the study and both surface ME signals (from the right vastus lateralis, vastus medialis, and rectus femoris muscles) and the biomechanical signals (force, position, and velocity) of an isokinetic dynamometer were measured. Two tests were performed: (i) 100 repetitive maximum isokinetic contractions of the right knee extensors, and (ii) five gradually increasing static knee extensions before and after (i). The corresponding ME signal time-frequency representations were calculated using the continuous wavelet transform. Compensation of the MNF variables of the repetitive contractions was performed with respect to the individual MNF-force relation based on an average of five gradually increasing contractions. Whether or not compensation was necessary was based on the shape of the MNF-force relationship. A significant compensation of the MNF was found for the repetitive isokinetic contractions. In conclusion, when investigating maximum dynamic contractions, decreases in MNF can be due to mechanisms similar to those found during sustained static contractions (force-independent component of fatigue) and in some subjects due to a direct effect of the change in force (force-dependent component of fatigue). In order to compare MNF shifts during sustained static and repetitive dynamic contractions it is necessary to estimate the force-dependent component of fatigue of dynamic contractions. Our results are preliminary and have to be confirmed in larger experiments using single dynamic contractions when determining the MNF-force relationship of the unfatigued situation.     

Intensity analysis in time-frequency space of surface myoelectric signals by wavelets of specified resolution

Surface myoelectric signals often appear to carry more information than what is resolved in root mean square analysis of the progress curves or in its power spectrum. Time-frequency analysis of myoelectric signals has not yet led to satisfactory results in respect of separating simultaneous events in time and frequency. In this study a time-frequency analysis of the intensities in time series was developed. This intensity analysis uses a filter bank of non-linearly scaled wavelets with specified time-resolution to extract time-frequency aspects of the signal. Special procedures were developed to calculate intensity in such a way as to approximate the power of the signal in time. Applied to an EMG signal the intensity analysis was called a functional EMG analysis. The method resolves events within the EMG signal. The time when the events occur and their intensity and frequency distribution are well resolved in the intensity patterns extracted from the EMG signal. Averaging intensity patterns from multiple experiments resolve repeatable functional aspects of muscle activation. Various properties of the functional EMG analysis were shown and discussed using model EMG data and real EMG data.     

The effects of four time-varying factors on the mean frequency of a myoelectric signal.

Daily activities involve dynamic muscle contractions that yield nonstationary myoelectric signals (MESs). The purpose of this work was to determine the individual effects of four time-varying factors (the number and firing rate of active motor units, muscle force and joint angle) on the mean frequency of a MES. Previous theoretical and experimental work revealed that although changes in the number and firing rate of active motor units contribute to the nonstationarities of the signal, they do not significantly affect the mean frequency. In the experimental work, 12 subjects performed 25 static contractions, one for each force (20, 30, 40, 50 and 60% of maximum voluntary contraction) and elbow joint angle (50, 70, 90, 110 and 130 degrees extension) combination. A MES was recorded from the surface of the biceps brachii during each contraction. The results indicated that muscle force only weakly affects the mean frequency. Also shown was that alteration in muscle geometry resulting from changes in elbow joint angle does significantly affect the mean frequency. Knowing this is important for the assessment of muscle fatigue during dynamic contractions.     

Optimal rejection of movement artefacts from myoelectric signals by means of a wavelet filtering procedure.

In this work the problem of rejection of motion artefacts from surface myoelectric signals, recorded during dynamic contractions, is studied. In fact, the extraction of frequency parameters and the detection of muscular activation patterns can be detrimentally affected by artefacts due to the movement of the surface electrodes, particularly stressed by the dynamic conditions of the exercise performed during measurement. In order to overcome this difficulty, four different filtering procedures have been tested and compared: a high-pass filtering procedure, a moving average procedure, a moving median procedure and a new adaptive wavelet based procedure, expressly designed for this work. Orthogonal Meyer wavelets are used with the aim of obtaining both a good reconstruction and a decomposition of the signal into non-overlapping bands. The four procedures have been tested with a set of different proofs utilising both synthetic and experimentally recorded myoelectric signals. The results show that the wavelet procedure performs better than the other methods both in information preservation and in time-detection. Moreover, the features of user-independence and adaptivity to the noise level suggest a wider range of applications of the proposed algorithm.     

Median frequency of the myoelectric signal. Effects of muscle ischemia and cooling

A study was performed to investigate the changes that occur in the median frequency of the myoelectric signal during local ischemia or reduction of intramuscular temperature produced by surface cooling. Data was obtained from experiments which involved the first dorsal interosseous muscle of 10 female and 16 male subjects. These subjects were asked to perform isometric constant-force abduction contractions of the index finger at 20% and 80% of maximal voluntary contraction level. The initial median frequency (IMF) of the myoelectric signal during the first 0.5 s of contraction was calculated. Results showed a significant reduction of the IMF in contractions performed under ischemic conditions; upon release, the IMF recovered quickly. At 80% maximal voluntary level of contraction, a greater decrease of the IMF was recorded. Similar results were demonstrated during reduction of intramuscular temperature with gradual recovery of the IMF after cooling. These results demonstrate that the median frequency of the myoelectric signal displays behavior similar to that reported for conduction velocity and this is consistent with the notion that accumulation of metabolic byproducts in muscle tissue causes a decrease in the conduction velocity of the muscle fibers.     

Correlation of average muscle fiber conduction velocity measured during cycling exercise with myosin heavy chain composition, lactate threshold, and VO2max.

The aim of the study was to investigate the correlation between myosin heavy chain (MHC) composition, lactate threshold (LT), maximal oxygen uptake VO2max, and average muscle fiber conduction velocity (MFCV) measured from surface electromyographic (EMG) signals during cycling exercise. Ten healthy male subjects participated in the study. MHC isoforms were identified from a sample of the vastus lateralis muscle and characterized as type I, IIA, and IIX. At least three days after a measure of LT and VO2max, the subjects performed a 2-min cycling exercise at 90 revolutions per minute and power output corresponding to LT, during which surface EMG signals were recorded from the vastus lateralis muscle with an adhesive electrode array. MFCV and instantaneous mean power spectral frequency of the surface EMG were estimated at the maximal instantaneous knee angular speed. Output power corresponding to LT and VO2max were correlated with percentage of MHC I (R2=0.77; and 0.42, respectively; P<0.05). MFCV was positively correlated with percentage of MHC I, power corresponding to LT and to VO2max (R2=0.84; 0.74; 0.53, respectively; P<0.05). Instantaneous mean power spectral frequency was not correlated with any of these variables or with MFCV, thus questioning the use of surface EMG spectral analysis for indirect estimation of MFCV in dynamic contractions.     

Effect of muscle length on surface EMG wave forms in isometric contractions

To elucidate the influence of muscle length on surface EMG wave form, comparisons were made of surface EMGs of the biceps and triceps brachii muscles during isometric contractions at different muscle lengths. Muscle lengths were altered by setting the elbow joint angle at several intervals between the limits of extension and flexion. The intensity of the isometric contractions was 25% of maximum voluntary contraction at the individual joint angles. Slowing was obvious in the EMG wave forms of biceps as muscle length increased. The so-called 'Piper rhythm' appeared when the muscle was more than moderately lengthened. The slowing trend with muscle lengthening, though less marked, was also seen in triceps. Zero-cross analysis revealed quasi-linear relationships between muscle length and slowing. Frequency analysis confirmed the development of 'Piper rhythm'. An attempt was made to interpret the slowing associated with muscle lengthening in terms of the propagation of myoelectric signals in muscle fibers. given the effect of muscle length on EMG wave forms, a careful control of joint angle may be required in assessing local making fatigue when using EMG spectral indices.     

The short-time Fourier transform and muscle fatigue assessment in dynamic contractions

The mean frequency of the power spectrum of an electromyographic signal is an accepted index for monitoring fatigue in static contractions. There is however, indication that it may be a useful index even in dynamic contractions in which muscle length and/or force may vary. The objective of this investigation was to explore this possibility. An examination of the effects of amplitude modulation on modeled electromyographic signals revealed that changes in variance created in this way do not sufficiently affect characteristic frequency data to obscure a trend with fatigue. This validated the contention that not all non-stationarities in signals necessarily manifest in power spectral parameters. While an investigation of the nature and effects of non-stationarities in real electromyographic signals produced from dynamic contractions indicated that a more complex model is warranted, the results also indicated that averaging associated with estimating spectral parameters with the short-time Fourier transform can control the effects of the more complex non-stationarities. Finally, a fatigue test involving dynamic contractions at a force level under 30% of peak voluntary dynamic range, validated that it was possible to track fatigue in dynamic contractions using a traditional short-time Fourier transform methodology.