EMG classification using wavelet functions to determine muscle contraction

Surface electromyogram (SEMG) is a complex signal and is influenced by several external factors/artifacts. The electromyogram signal from the stump of the subject is picked up through surface electrodes. It is amplified and artifacts are removed before digitising it in a controlled manner so that minimum signal loss occurs due to processing. As removing these artifacts is not easy, feature extraction to obtain useful information hidden inside the signal becomes a different process. This paper presents methods of analysing SEMG signals using discrete wavelet Transform (DWT) for extracting accurate patterns of the signals and the performance of the used algorithms is being analysed rigorously. The obtained results suggest a root mean square difference (RMSD) value for the denoising and quality of reconstruction of the SEMG signal. The result shows that the best mother wavelets for tolerance of noise are second order of symmlets and bior6.8. Results inferred that bior6.8 suitable for the classification and analysis of SEMG signals of different arm motions results in a classification accuracy of 88.90%.     

Spectral trimming technique: a new approach for suppressing motion artefacts in stress electrocardiography

Abstract

Motion artefacts in electrocardiographic (ECG) signal are suppressed mainly by adaptive noise cancellation and wavelet denoising. While the former requires a motion sensor in addition to ECG electrodes, the latter removes some of the desired low-frequency components in the signal. In this paper spectral trimming technique is being introduced for suppressing the motion artefacts in stress electrocardiography. In this method, Fourier spectral coefficients up to 1.221?Hz of noisy signal are trimmed on the basis of template derived from resting ECG signal in the same subject. The proposed spectral trimming technique has yielded the lowest value of mean?±?standard deviation for root mean square error (18.92?±?8.71) and highest value of the signal to noise ratio (6.439?±?4.266) as compared to other three methods, namely adaptive noise cancellation, wavelet decomposition and adaptive line enhancement with compatible value of correlation coefficient. Subsequently, spectral trimming technique has been implemented in real-time (deferred by 8.2?s) application for stress electrocardiography. Spectral trimming technique thus offers a method of choice for motion artefact suppression in offline as well as deferred online applications. This method takes care of the limitations of conventional methods such as adaptive noise cancellation or wavelet denoising for suppressing motion artefacts in stress electrocardiography.     

基于小波阈值法的脉搏波去噪算法

目的消除可穿戴式脉搏波监测设备在连续测量中由于运动造成的运动伪差,保证设备准确性和稳定性。方法通过选取合适的小波基、小波最大分解层数、阈值函数和阈值方法,对脉搏波信号进行小波阈值处理,提出了一种基于小波阈值法去除脉搏波噪声的算法。并针对在脉搏波信号采集过程中出现的基线漂移、工频干扰和运动伪差,与加窗傅里叶变换去噪后的结果进行对比。结果在信噪比、均方差和平滑度等关键指标上,小波阈值法的效果更优。利用db9小波基对脉搏波信号进行6层小波分解,设置启发式阈值所得到的处理效果最好。结论该算法能够有效抑制工频干扰和运动干扰,使信噪比提高22 dB,均方差接近于0,且平滑度降为原来的11%,实现脉搏波信号采集中干扰的有效去除。     

Surface electromyogram spectral characterization and motor unit activity during voluntary ramp contraction in men

Summary The relationships were investigated between the surface electromyographic (SEMG) power spectrum analysed by the 20 order autoregressive model (AR spectrum) and underlying motor unit (MU) activity during isometric contractions increasing linearly from 0% to 80% maximal voluntary contraction. Intramuscular spikes and SEMG signals were recorded simultaneously from biceps brachii muscle; the former were analysed by a computer-aided intramuscular MU spike amplitude-frequency (ISAF) histogram and the latter subjected to AR spectral analysis. Results indicated that there was a positive correlation between the force output and the mean amplitude of the ISAF histogram but not with the mean frequency. These changes were accompanied by changes in relative power of the high frequency (100–200 Hz) peak (HL) in the AR spectrum. It was also found that there was a positive correlation between the mean amplitude of the ISAF histogram and the HL value. These data suggested that the power of the high frequency peak in the AR spectrum of the SEMG signal preferentially reflected the progressive recruitment of underlying MU according to their size. Differences between the AR spectrum and the spectrum estimated by fast Fourier transform algorithm have also been discussed.     

Interindividual variability of surface EMG changes during cycling exercise in healthy humans.

We studied surface electromyogram (SEMG) changes during 1-h endurance cycling in 12 healthy subjects of whom five were involved in mountain bike training programme. The work load was set at 50% of the predicted maximal heart rate. The surface EMG and the compound evoked muscle action potential (M-wave) from the vastus lateralis muscle were recorded at rest, during the 1-h cycling period, and the 20-min recovery period. The root mean square (RMS) and the median frequency (MF) of SEMG power spectrum were computed. In all subjects, there was no shift in the median frequency throughout the cycling period and the increase in RMS remained stable. In subjects untrained to endurance cyclism, the M-wave duration increased at the end of the cycling period and these changes persisted for a consecutive 15-min period during recovery of exercise. By contrast, in trained mountain bikers the M-wave duration decreased after 2 min of exercise--the effect persisting for 2 min during recovery. These data suggest that the interpretation of M-wave changes during cycling must take into consideration the sport practices of the subjects and also that SEMG power spectrum and M-wave explore different electrophysiological events.     

Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review

Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time–frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time–frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.

     

基于经验模态分解-小波包变换的表面肌电信号手势识别

为了提高表面肌电信号（sEMG）手部运动识别的正确率,比较常规的sEMG预处理和特征提取方法,提出一种基于经验模态分解（EMD）和小波包变换（WPT）的sEMG手势识别模型。首先,使用EMD方法将sEMG进行平稳化,得到一系列的固有模态函数。其次,求取各个固有模态函数与原始信号的相关性,选取相关性较高的前4个分量作为有效分量。然后,采用Db3小波函数进行WPT,提取小波包系数中的平均能量、平均绝对值、最大值、均方根和方差等特征。分别采用线性判别分析和支持向量机对12种手部运动进行模式识别。结果表明基于EMD和WPT的sEMG手势识别正确率比直接提取小波包系数中的特征识别正确率高。     

宫缩与非宫缩状态下子宫肌电信号特征研究

目的利用体表子宫肌电信号的分析实现宫缩和非宫缩状态的识别。方法利用Monica AN24母胎监护仪采集10名孕期和10名临产期孕妇的子宫肌电信号,然后提取了子宫肌电信号线性和非线性特征参数及其变化率,特征参数包括均方根、峰值频率、中值频率、平均频率、小波包分解系数方差和时间可逆性。使用统计学方法对提取的数据特征进行单因素方差分析,比较了临产组和孕期组中宫缩段和非宫缩段信号的差异。结果组内宫缩段信号与非宫缩段信号、组间宫缩段信号与非宫缩段信号的特征差异具有统计学意义(P0.05)。结论子宫肌电信号的研究为子宫收缩的识别与监测提供了重要的参考价值。     

Respiratory and stress-induced activation of low-threshold motor units in the human trapezius muscle

The study aimed to characterize trapezius motor unit firing pattern in low-amplitude contractions, with emphasis on respiratory modulated activity. Constant-amplitude contractions with shoulder elevation, controlled by feedback of the root mean square detected surface electromyographic (SEMG) signal, typing with arm movement and tasks with mental stress were performed. Single motor unit activity was recorded by a quadrifilar fine-wire electrode. A surface electrode simultaneously recorded SEMG activity. Contraction amplitudes ranged from 1 to 10% of the SEMG signal at maximum voluntary contraction (1–10% EMG_max). The majority (∼80%) of motor units recorded during constant-amplitude contractions showed firing rate modulation at the respiratory frequency. Respiratory firing rate modulation was clear for low amplitude contractions (< 3% EMG_max), but was reduced at higher amplitudes (3–5.9% EMG_max). Most motor units had peak firing rate at the transition from inspiration to expiration, but peak firing rate at the transition from expiration to inspiration or at the first harmonic frequency was also observed. The SEMG signal showed little or no respiratory modulation, possibly because respiratory phase varied between motor units. Respiratory modulation of firing rates was significantly reduced in experiments with mental stress and was rarely observed in typing experiments. Both central respiratory drive and peripheral afferent input may contribute to respiratory modulation of firing rates; however, animal studies indicate a central source of the respiratory modulated input. We speculate that the reduction in respiratory modulation of motor activity with mental stress is due to activation of alternative pathways providing excitatory input to trapezius motoneurons.     

Research on the Method of EEMD in Pulse Wave Signals Processing

The mixed noise in the acquisition process of pulse wave signals will affect the signal analysis, how to effectively eliminate the noise and complete the pulse wave analysis has important practical significance. In this paper, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) were used to realize scale decomposition of pulse wave signals to obtain intrinsic mode function(IMF). A band-pass filter was implemented according to the characteristic time scale parameters of the IMF. After filtering and reconstruction, the pulse wave denoising was completed. The denoising effects of EMD, EEMD and wavelet transform were compared in terms of mean square error and signal-to-noise ratio. The result shows that EMD and EEMD are better than wavelet transform, and the effects are similar. Further comparing the Hilbert-Huang spectrum of EMD and EEMD, it can be seen that EEMD can not only avoid mode mixing, but also facilitate the analysis of pulse wave signals.     

Nonlinear analysis of the performance and reliability of wavelet singularity detection based denoising for Doppler ultrasound fetal heart rate signals

Many studies on the physiology of the cardiovascular system revealed that nonlinear chaotic dynamics govern the generation of the heart rate signal. This is also valid for the fetal heart rate (FHR) variability, where however the variability is affected by many more factors and is significantly more complicated than for the adult case. Recently an adaptive wavelet denoising method for the Doppler ultrasound FHR recordings has been introduced. In this paper the performance and reliability of that method is confirmed by the observation that for the wavelet denoised FHR signal, a deterministic nonlinear structure, which was concealed by the noise, becomes apparent. It provides strong evidence that the denoising process removes actual noise components and can therefore be utilized for the improvement of the signal quality. Hence by observing after denoising a significant improvement of the 'chaoticity' of the FHR signal we obtain strong evidence for the reliability and efficiency of the wavelet based denoising method. The estimation of the chaoticity of the FHR signal before and after the denoising is approached with three nonlinear analysis methods. First, the rescaled scale analysis (RSA) technique reveals that the denoising process increases the Hurst exponent parameter as happens when additive noise is removed from a chaotic signal. Second, the nonlinear prediction error evaluated with radial basis function (RBF) prediction networks is significantly lower at the denoised signal. The significant gain in predictability can be attributed to the drastic reduction of the additive noise from the signal by the denoising algorithm. Moreover, the evaluation of the correlation coefficient between actual and neural network predicted values as a function of the prediction time displays characteristics of chaos only for the denoised signal. Third, a chaotic attractor, reconstructed with the embedding dimension technique, becomes evident for the denoised signal, while it is completely obscured for the original signals. The correlation dimension of the reconstructed attractor for the denoised signal tends to reach a value independent of the embedding dimension, a sign of deterministic chaotic signal. In contrast for the original signal the correlation dimension increases steadily with the embedding dimension, a fact that indicates strong contribution of noise.     

基于多尺度最大李雅普诺夫指数的表面肌电信号模式识别

为了提高动作表面肌电信号的识别率,提出一种将最大李雅普诺夫指数和多尺度分析结合的方法。从非线性和非平稳的角度出发,引入多尺度最大李雅普诺夫指数特征,并应用到人体前臂6类动作表面肌电信号的模式识别中。首先利用希尔伯特-黄变换,对原始信号进行经验模态分解,即多尺度分解;然后利用非线性时间序列分析方法,计算多尺度最大李雅普诺夫指数;最后将多尺度最大李雅普诺夫指数作为特征向量,输入支持向量机进行识别。平均识别率达到97.5%,比利用原始信号的最大李雅普诺夫指数进行识别时提高了3.9%。结果表明,利用多尺度最大李雅普诺夫指数对动作表面肌电信号进行模式识别效果良好。     

The influence of contraction amplitude and firing history on spike-triggered averaged trapezius motor unit potentials

The spike-triggered averaged (STA) technique was used to examine trapezius motor unit potentials and their dependence on contraction amplitude and firing history. Individual motor unit firings were identified by a fine-wire intramuscular electrode, while STA-derived potentials were extracted from the simultaneously recorded surface electromyographic (SEMG) signal. Amplitude-controlled contractions and contractions with typing tasks and mental stress were carried out. STA potentials were mostly derived from 20 s intervals of firing. Motor unit synchrony was estimated by peristimulus time histograms (PSTHs). An association between SEMG amplitude and STA-derived motor unit potentials was found: motor unit area showed a four-fold increase when SEMG amplitude increased from 1.5 to 10.5% of the root mean square-detected SEMG signal at maximal voluntary contraction (%EMG_max). Low- and higher threshold motor unit potentials, all with recruitment thresholds <10% EMG_max, had similar area at the same contraction amplitude. A significant increase in the STA-derived potentials was observed after 3 min of constant-amplitude contractions; however, this difference was reduced after 10 min and no longer present after 30 min of contraction. Motor unit synchrony accounted for, on average, 2.8% additional firings within 2 ms of the triggering motor unit. We conclude that the increase in STA-derived potentials with contraction amplitude is, to a major extent, due to motor unit synchrony, limiting the applicability of this method in postural muscles presenting wide motor unit potentials. The similar area of motor units at same SEMG amplitude may indicate that trapezius motor units recruited below 10% EMG_max are of similar size and thus not organized according to the Henneman size principle.     

Electromyographic analysis of reach in individuals with cerebral palsy

Electromyographic analysis in both the time domain (root mean square EMG) and the frequency domain (mean power frequency EMG) of the biceps, triceps, wrist extensors and wrist flexors were analysed in six young cerebral palsied adults and six normal individuals. The subjects sat in a Rifton positioning chair. Each subject's right arm was positioned with the shoulder adducted, the elbow at 90 degrees and the hand resting on the arm rest. The subject then reached the right arm forward to grasp a dowel which was placed at shoulder level in front of the subject. There was no significant difference between the time it took the two groups to do the required movement. The RMS analysis indicated the muscle activation was variable among subjects, with evidence of concontraction of the antagonist muscles for the disabled group. The frequency analysis indicated that the disabled group had significantly lower mean power for the biceps and the wrist extensor muscles compared to the normal group. Neurological differences or fibre type abnormalities may account for these differences.     

基于Hilbert-Huang变换的医学超声信号去噪

目的:探求一种基于Hilbert-Huang变换的医学超声信号去噪方法。方法:提出了一种基于Hilbert-Huang变换的医学超声信号去噪方法。首先对含噪超声信号进行经验模式分解,得到各阶IMF分量,然后对高频的IMF分量用阈值方法进行处理,把经过阈值处理的高频的IMF分量和低频IMF分量进行叠加,得到重构的去噪信号。结果:仿真实验表明,基于Hilbert-Huang变换的医学超声信号去噪方法可以有效地降噪。结论:Hilbert-Huang变换的医学超声信号去噪方法在自适应性和先验性方面优于基于小波的去噪方法。     

Evaluation of muscle fatigue of wheelchair basketball players with spinal cord injury using recurrence quantification analysis of surface EMG

Wheelchair basketball is the most popular exercise activity among individuals with spinal cord injury (SCI). The purpose of this study was to investigate muscular endurance and fatigue in wheelchair basketball athletes with SCI using surface electromyography (SEMG) and maximal torque values. SEMG characteristics of 10 wheelchair basketball players (WBP) were compared to 13 able-bodied basketball players and 12 sedentary able-bodied subjects. Participants performed sustained isometric elbow flexion at 50% maximal voluntary contraction until exhaustion. Elbow flexion torque and SEMG signals were recorded from three elbow flexor muscles: biceps brachii longus, biceps brachii brevis and brachioradialis. SEMG signals were clustered into 0.5-s epochs with 50% overlap. Root mean square (RMS) and median frequency (MDF) of SEMG signals were calculated for each muscle and epoch as traditional fatigue monitoring. Recurrence quantification analysis was used to extract the percentage of determinism (%DET) of SEMG signals. The slope of the %DET for basketball players and WBP showed slower increase with time than the sedentary able-bodied control group for three different elbow flexor muscles, while no difference was observed for the slope of the %DET between basketball and WBP. This result indicated that the athletes are less fatigable during the task effort than the nonathletes. Normalized MDF slope decay exhibited similar results between the groups as %DET, while the slope of the normalized RMS failed to show any significant differences among the groups (p?>?0.05). MDF and %DET could be useful for the evaluation of muscle fatigue in wheelchair basketball training. No conclusions about special training for WBP could be determined.     

Baseline drift removal and denoising of MCG data using EEMD: Role of noise amplitude and the thresholding effect

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).     

基于最佳小波包的表面肌电信号分类方法

针对表面肌电信号的分类问题，采用最佳小波包分解构造最能体现分类能力的小波包基。用Fisher线性判别分析对肌电信号各个子空间的相对能量特征进行降维处理，然后利用BP神经网络进行分类识别。实验表明该方法能够有效地从伸肌和屈肌采集的两道肌电信号中识别前臂内旋、前臂外旋、握拳和展拳四种运动模式，是一种稳定、有效的特征提取方法，为非平稳生理信号的分析提供了新的手段。     

Selection of mother wavelet and denoising algorithm for analysis of foetal phonocardiographic signals

This paper is aimed at the selection of suitable mother wavelet and denoising algorithm for the analysis of foetal phonocardiographic (fPCG) signals. Fourier based analysing tools have some limitations concerning frequency and time resolutions. Although wavelet transform (WT) overcomes these limitations, it requires proper selection of a mother wavelet and denoising algorithm. In this study a suitable mother wavelet is selected on the basis of properties of different wavelet families and characteristics of the fPCG signals. The universal threshold, minimax threshold and rigorous SURE threshold algorithms along with soft or hard thresholding rule have been compared to denoise these signals. The mean squared error (MSE) is used to evaluate the performance of these algorithms. The results show that the fourth order Coiflets wavelet has a better performance for the analysis of fPCG signals when using the rigorous SURE threshold denoising algorithm with soft thresholding rule. The proposed approach is simple and proves to be effective when applied to the selection of suitable mother wavelet and denoising algorithm for the fPCG signals. These denoised signals can be used for the accurate determination of foetal heart rate (FHR) and further diagnostic applications of the foetus.     

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform

Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and average PRD of 3.14. Comparison between the obtained results and recently published results show that the proposed technique gives better performance.