表面肌电检测中消除工频干扰的方法

从硬件和软件数字信号处理方面介绍了表面肌电检测中消除工频干扰的方法,硬件上采用了共模屏蔽驱动电路,模拟陷波器;数字信号处理方面应用自适应数字陷波器和谱内插的方法.模拟陷波器和数字陷波器由于消除工频干扰的同时也去除了信号成分,一般只在简单生物反馈中使用.谱内插的方法是在假设真实信号的50Hz成分可以通过内插法由相邻的频率成分估计出,这样就可以消除工频干扰,而且即使工频干扰的相位发生变化也可很好的消除其影响.     

双通道表面肌电信号采集装置的设计与分析

本研究设计了一种双通道的表面肌电信号(surface electromyography,sEMG)采集装置.该装置以STM32为主控芯片,配以sEMG采集模块,实现对肌电信号的采集,并将数据传至由MATLAB编程的上位机进行分析处理.该装置对于人体内部及周围环境干扰噪声的处理均在硬件上实现,信噪比约为60～70 dB....     

Optimal signal bandwidth for the recording of surface EMG activity of facial, jaw, oral, and neck muscles

Spontaneous pericranial electromyographic (EMG) activity is generally small and is contaminated by strong low-frequency artifacts. High-pass filtering should suppress artifacts but affect EMG signal power only minimally. In 24 subjects who performed a warned simple reaction time task, the optimal high-pass cut-off frequency was examined for nine different pericranial muscles. From four experimental conditions (visual and auditory reaction signals combined with hand and foot responses), 1-min EMG recordings were selected (bandwidth: 0.4-512 Hz) and divided into 60 1-s data segments. These segments were high-pass filtered, the -3-dB cut-off frequency varying from 5 to 90 Hz, and subjected to power spectral analysis. Optimal high-pass filter frequencies were determined for the mean power spectra based on visual estimation or comparison with a theoretical spectrum of the artifact-free EMG signal. The optimal frequencies for the different muscles varied between 15 and 25 Hz and were not influenced by stimulus or response modality. For all muscles, a low-pass filter frequency between 400 and 500 Hz was appropriate.     

针对微弱表面肌电信号的采集电路设计

表面肌电信号频率低、极易受干扰，针对此设计基于高阶滤波的信号采集电路。为避免噪声被过度放大而造成信号淹没，电路采用两级放大方案。带通滤波部分采用两组5阶Sallen-Key，以奇次在前、偶次在后的顺序级联，避免输出信号中混入高频泄露信号，且阻带下降速度达-100 dB/dec。此外，陷波器部分设置可调电位器，灵活调整品质因数（Q值），以获得最佳采集效果。结果表明，该电路可实现高达60 dB的放大增益，可有效提取20~500 Hz之间的有用信号，同时很好地抑制50 Hz工频干扰，具有良好的抗噪声性能。     

Single motor unit analysis from spatially filtered surface electromyogram signals. Part 2: Conduction velocity estimation

The aim of the study was to compare experimentally conduction velocity (CV) estimates obtained with different estimation methods based on surface electromyogram (EMG) signals detected using five spatial filters. The filters investigated were the longitudinal single and double differential, transverse single and double differential, and normal double differential. The same surface EMG signals detected as described in Part 1 were used in this work. CV was estimated with four commonly used delay estimation techniques, i.e. from the distance between the peak values of two waveforms (with and without polynomial interpolation around the peak), and by the maximum likelihood estimate (MLE) based on two or more surface EMG channels. The average standard deviation of CV estimation (for all the MUs and the two muscles together) was 0.61 ms⁻¹ and 0.79 ms⁻¹ for the peak method, with and without interpolation, respectively, and 0.50ms⁻¹ and 0.31 ms⁻¹ for the MLE method, from two and more surface EMG channels, respectively. Moreover, the mean of CV estimates varied by as much as 1 ms⁻¹ depending on the spatial filter used and the method adopted for CV estimation. Considering the dependence on the spatial filter only, the average (over all estimation methods) CV estimates obtained with the five spatial filters were 4.32 ms⁻¹ (normal double differential), 4.23ms⁻¹ (longitudinal double differential), 4.61 ms⁻¹ (transverse double differential), 4.64ms⁻¹ (transverse single differential) and 4.03 ms⁻¹ (longitudinal single differential). It was concluded that the comparison of single MU CV values obtained in different studies is critical if different spatial filters and processing techniques are used for their estimation. Higher estimates of CV were attributed to a smaller reduction in non-travelling signal components and thus were assumed to be positively biased.     

Optimal EMG signal bandwidth and interelectrode distance for the recording of acoustic, electrocutaneous, and photic blink reflexes

The bandwidth for the recording of the orbicularis oculi blink reflex electromyogram (EMG) response is optimal when low-frequency artifacts, such as motion artifacts and cross-talk from other muscles, are maximally suppressed, whereas true EMG signal power is maximally retained. The optimal bandwidth was investigated for acoustic, electrocutaneous, and photic blink reflexes. Reflexes were recorded with varying bandwidth and interelectrode distances of 12 and 36 mm. Power spectra of the EMG signals were calculated and compared with a theoretical spectrum of the uncontaminated EMG signal. For both electrode distances, the optimal bandwidth was on the average 28–500 Hz for acoustic and electrocutaneous blink reflexes and 12–500 Hz for photic blinks. Using photic stimuli, however, a high-pass filter frequency larger than 12 Hz (probably at least 30 Hz) in combination with occlusion of the eye will be necessary to avoid influences of retinal potentials. Given the optimal bandwidth, a larger electrode spacing may be expected to moderately improve the detectability of small blinks in all stimulus conditions.     

Pseudo-real-time low-pass filter in ECG,self-adjustable to the frequency spectra of the waves

The electrocardiogram (ECG) acquisition is often accompanied by high-frequency electromyographic (EMG) noise. The noise is difficult to be filtered, due to considerable overlapping of its frequency spectrum to the frequency spectrum of the ECG. Today, filters must conform to the new guidelines (2007) for low-pass filtering in ECG with cutoffs of 150 Hz for adolescents and adults, and to 250 Hz for children. We are suggesting a pseudo-real-time low-pass filter, self-adjustable to the frequency spectra of the ECG waves. The filter is based on the approximation procedure of Savitzky–Golay with dynamic change in the cutoff frequency. The filter is implemented pseudo-real-time (real-time with a certain delay). An additional option is the automatic on/off triggering, depending on the presence/absence of EMG noise. The analysis of the proposed filter shows that the low-frequency components of the ECG (low-power P- and T-waves, PQ-, ST- and TP-segments) are filtered with a cutoff of 14 Hz, the high-power P- and T-waves are filtered with a cutoff frequency in the range of 20–30 Hz, and the high-frequency QRS complexes are filtered with cutoff frequency of higher than 100 Hz. The suggested dynamic filter satisfies the conflicting requirements for a strong suppression of EMG noise and at the same time a maximal preservation of the ECG high-frequency components.     

自适应算法与小波变换在心电信号滤波中的应用

心电信号是一种基本的人体生理信号,具有重要的临床诊断价值。然而,体表检测人体心电信号中常带有工频干扰、基线漂移、肌电干扰等各种噪声,给临床对心血管疾病的诊断带来了障碍。为了消除心电信号检测过程中带有的上述三种噪声,采用LM S自适应算法及小波变换理论,有针对性的设计了自适应滤波器、小波变换滤波器和自适应信号分离器等三种数字滤波器来滤除相应干扰。结果表明,对心电信号中存在的这三种噪声具有很好的滤波效果。     

生理信号检测中的有源工频陷波器

本文讨论了文氏电桥陷波器的品质因素Q和陷波深度D之间的关系,并从参数灵敏度的角度分析了元件的制造容差对电路D、Q特性的影响情况。在此基础上,介绍了一种性能优越的陷波电路。该电路能够达到的陷波深度及品质因素取决于有源器件的开环增益。     

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.     

Hand dominance during constant force isometric contractions: evidence of different cortical drive commands

The purpose of this study was to investigate force variability and sensoriomotor strategies of dominant and nondominant hands of right and left-handed subjects during a submaximal isometric force production task. Twelve right-handed adults (9 men and 3 women; 23?±?3?year) and twelve left-handed adults (4 men and 8 women; 24?±?3?year) performed an isometric constant force contraction sustained at 30 and 50% of maximal force for 10?s. Surface EMG signals were obtained from forearm flexors and extensors. Force signals were analyzed in the time (CV of force) and frequency (0-10?Hz) domain. The neural activation of the involved muscles was investigated from the EMG structure using the cross-wavelet spectra of the interference EMG signals of six different frequency bands of the EMG signals were quantified (5-13, 13-30, 30-60, 60-100, 100-150 and 150-200?Hz). The major findings were: (1) dominant and nondominant hands of right- and left-handed subjects exhibited similar CV of force; (2) the power spectrum of force is influenced by handedness, with greater 1-3?Hz oscillations for left-handed subjects when compared to right-handed subjects; (3) right-handed subjects have greater 30-60?Hz neuromuscular activation when compared to left-handed subjects. Our results indicate that right-handed individuals may rely preferentially in visual feedback to carry out a task with visual and proprioceptive feedback because of the left hemisphere specialization on the visuomotor control.     

一种基于提升小波和中值滤波的心电去噪方法

小波变换在心电去噪中有非常好的效果,但传统的小波变换计算量大,不利于实时处理和嵌入式系统的实现,提升小波是一种快速有效的小波变换的实现方法,本文提出了一种运用提升小波和中值滤波去除心电信号工频干扰、肌电干扰和基线漂移三种噪声的方法。该方法运用提升小波对含噪声的心电信号做三层分解,并根据小波基的特性在不同层次采用不同的小波基,去除心电信号的工频干扰和肌电干扰;对第三层分解后得到的数据做中值滤波,去除心电信号的基线漂移。将以上方法与传统的小波方法相比,去噪结果表明两者去噪效果相当,但提升方法运算速度有很大的提升。结果证实将提升小波与中值滤波方法结合可以有效地去除心电信号的工频干扰、肌电干扰和基线漂移,而且可以较大地提高运算速度,便于进行实时处理和嵌入式系统的实现。     

Removal of cardiac beat artifact in oesophageal pressure measurement by frequency analysis

Oesophageal pressure (P_es) measurements are important in medical research and useful in clinical diagnosis. Measurements, however, are contaminated heavily by cardiac artifacts. The spectrum and waveform of the P_es signal is obtained from the oesophageal balloon. Adaptive finite impulse response (AFIR) filter and modified adaptive noise cancellation (MANC) methods are adopted to filter out cardiac beat interference. These results are compared. In the frequency domain, frequency variations and spectral overlap between the P_es components and cardiac beat signal components impact on the performance of the filter. From our experimental results on power strength, the fourth or higher harmonics did not have any significant effect on the filter performance. However, the second harmonics of these signals had a significant effect on the filtering result. Thus, in the design of AFIR filters, attention is needed to minimise these effects. In frequency analysis, these harmonics or overlapping frequencies do not affect MANC. MANC was the better method for eliminating cardiac beat artifact in P_es measurement. The dynamic compliance (C_dyn) was also used to evaluate the performance of MANC and AFIR. The standard deviation of C_dyn was less than 0.15 using MANC, compared with standard deviations as high as 0.57 for AFIR. We conclude that MANC performs better than AFIR.     

Removal of cardiac beat artifact in oesophageal pressure measurement by frequency analysis

Oesophageal pressure (Pes) measurements are important in medical research and useful in clinical diagnosis. Measurements, however, are contaminated heavily by cardiac artifacts. The spectrum and waveform of the Pes signal is obtained from the oesophageal balloon. Adaptive finite impulse response (AFIR) filter and modified adaptive noise cancellation (MANC) methods are adopted to filter out cardiac beat interference. These results are compared. In the frequency domain, frequency variations and spectral overlap between the Pes components and cardiac beat signal components impact on the performance of the filter. From our experimental results on power strength, the fourth or higher harmonics did not have any significant effect on the filter performance. However, the second harmonics of these signals had a significant effect on the filtering result. Thus, in the design of AFIR filters, attention is needed to minimise these effects. In frequency analysis, these harmonics or overlapping frequencies do not affect MANC. MANC was the better method for eliminating cardiac beat artifact in Pes measurement. The dynamic compliance (Cdyn) was also used to evaluate the performance of MANC and AFIR. The standard deviation of Cdyn was less than 0.15 using MANC, compared with standard deviations as high as 0.57 for AFIR. We conclude that MANC performs better than AFIR.     

Relation between EMG power spectrum shifts and muscle fibre action potential conduction velocity changes during local muscular fatigue in man

Summary Muscular fatigue was studied in the human m. biceps brachii by contracting the muscle as long as possible at 50% of maximum voluntary strength. For 8 subjects changes in the muscle fibre action potential conduction velocity could successfully be measured using the cross-correlation technique between two surface EMG signals. Shifts in the EMG power spectrum were quantified using the mean power frequency (MPF) of the EMG power spectral density function.During fatigue the EMG power spectrum gradually shifted to lower frequencies. The mean value and standard deviation of the MPF value decreased from (115±20) Hz at the beginning of the experiment to (60±18) Hz at the end. For 4 of the 8 subjects the decrease in MPF value was accompanied by a substantial decrease in conduction velocity (mean decrease was 33% of the initial velocity value). For the other 4 subjects, despite the great MPF changes, there was hardly any change in conduction velocity (mean decrease was 3%).The present study shows that great EMG power spectral shifts during muscular fatigue may occur without a concomitant change in muscle fibre action potential conduction velocity.     

Application of a wavelet adaptive filter to minimise distortion of the ST-segment

A wavelet adaptive filter (WAF) for the removal of baseline wandering in ECG signals is described. The WAF consists of two parts. The first part is a wavelet transform that decomposes the ECG signal into seven frequency bands using Vaidyanathan-Hoang wavelets. The second part is an adaptive filter that uses the signal of the seventh lowest-frequency band among the wavelet transformed signals as primary input and a constant as reference input. To evaluate the performance of the WAF, two baseline wandering elimination filters are used, a commercial standard filter with a cutoff frequency of 0.5 Hz and a general adaptive filter. The MIT/BIH database and the European ST-T database are used for the evaluation. The WAF performs better in the average power of eliminated noise than the standard filter and adaptive filter. Furthermore, it shows a lower ST-segment distortion than the standard filter and the adaptive filter.     

Continuous percutaneous measurement by laser-Doppler flowmetry of skeletal muscle microcirculation at varying levels of contraction force determined electromyographically

Summary Laser-Doppler flowmetry (LDF) and electromyography (EMG) were used simultaneously for measuring skeletal muscle blood perfusion in relation to static load and fatigue. Percutaneous single-fibre LDF and bipolar surface EMG of the trapezius muscle were performed continuously during a 10-min series of alternating periods of static contractions and rest, each of 1-min duration. The muscle was exposed to static load expressed as shoulder torque, by keeping the arms straight and elevated at 30, 60, 90 and 135°. On-line computer processing of the LDF and EMG signals made possible the interpretation of the relationship between the perfusion and the activity of the muscle. The LDF and root mean square (rms)-EMG were normalized by using the average value of the serial examinations of each individual as a reference value. Spectrum analyses of EMG showed the lowest variability for median frequency (MDF) in the frequency range 10–1000 Hz and mean power frequency (MPF) at 2–1000 Hz. The LDF power spectrum density during low (muscle rest) and high (high-force muscle contraction) perfusion indicated that disturbances were small when measurements were performed during sustained static contraction with as little movement as possible. Vasomotion, i.e. rhythmic variations in the blood flow, were present and showed a frequency of 5–6 cycles · min^–1. Application of a tourniquet to the upper arm caused an arrest of the microcirculation in the distally situated brachioradial muscle which was followed by a postischaemic hyperaemia upon removal of the torniquet. In ten healthy men, regression analyses showed positive correlation between rms-EMG and shoulder torque (r=0.77), negative correlation between MPF and arm elevation angle (r= –0.89) indicating accumulated fatigue, and almost positive correlations between LDF and rms-EMG (r=0.65), and between LDF and shoulder angle (r=0.67) when the right trapezius muscle was examined.     

基于高阶谱的肌纤维传导速度谱坑法检测

针对目前采用的谱坑法抗噪声性能差的问题,提出了基于高阶谱重构功率谱的谱坑法,检测肌纤维传导速度(MFCV),并对这种方法的抗噪性能进行了研究.同时,提出一种基于小波多尺度分析的适于噪声环境下谱坑自动检测的多尺度-二阶差分法,完成对肌纤维传导速度的检测.仿真实验结果表明,本文的方法与经典周期图估计功率谱谱坑法相比,可以显著改善估计功率谱的信噪比,提高谱坑法的抗噪能力和MFCV检测的精度.对真实针电极肌电信号的实验结果也表明,利用这种检测方法得到的MFCV数值与通常给出的MFCV范围相符.     

Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations

Muscle modelling is an important component of body segmental motion analysis. Although many studies had focused on static conditions the relationship between electromyographic (EMG) signals and joint torque under voluntary dynamic situations has not been well investigated. The aim of this study was to investigate the performance of a recurrent artificial neural network (RANN) under voluntary dynamic situations for torque estimation of the elbow complex. EMG signals together with kinematic data, which included angle and angular velocity, were used as the inputs to estimate the expected torque during movement. Moreover, the roles of angle and angular velocity in the accuracy of prediction were investigated, and two models were compared. One model used EMG and joint kinematic inputs and the other model used only EMG inputs without kinematic data. Six healthy subjects were recruited, and two average angular velocities (60^o s⁻¹ and 90^o s⁻¹) with three different loads (0 kg, 1 kg, 2 kg) in the hand position were selected to train and test the RANN between 90^o elbow flexion and full elbow extension (0^o). After training, the root mean squared error (RMSE) between expected torque and predicted torque of the model, with EMG and joint kinematic inputs in the training data set and the test data set were 0.17±0.03 Nm and 0.35±0.06 Nm, respectively. The RMSE values between expected torque and predicted torque of the model, with only EMG inputs in the training data set and the test set, were 0.57±0.07 Nm and 0.73±0.11 Nm, respectively. The results showed that EMG signals together with kinematic data gave significantly better performance in the joint torque prediction; joint angle and angular velocity provided important information in the estimation of joint torque in voluntary dynamic movement.     

Multichannel recording of cardiac potentials

An inexpensive system for the simultaneous recording of 256 cardiac electrograms is described. Time division multiplexing of cardiac potentials by digital circuitry provides a single video signal which is recorded on video tape. During playback a demultiplexer reassembles the separate signals. Measured channel characteristics demonstrate a bandwidth of 170 Hz, a noise level of 0·2 mV peak-to-peak, and a crosstalk of −27·5 dB. The design has flexibility in that individual channel bandwidth can be exchanged with the total number of channels according to the dictates of signal requirements. To illustrate this flexibility a second system is described which provides 64 channels whose measured characteristics include an 880 Hz bandwidth, a noise level of 0·14 mV peak-to-peak and a crosstalk of −33 dB.