A comparative study of simultaneous vibromyography andelectromyography with active human quadriceps

Vibromyographic (VMG) signals, which are low-frequency vibration signals generated during muscle contraction, were studied in comparison with electromyographic (EMG) signals recorded simultaneously during isometric contraction of the human quadriceps muscles. The comparison was accomplished by evaluating the averaged root mean squared (rms) value, mean frequency (MF), and peak frequency (PF) of the VMG and EMG signals for four muscle contraction levels at joint angles of 30 degrees, 60 degrees, and 90 degrees. The four contraction levels, namely 20, 40, 60, and 80% of maximum voluntary contraction (MVC), were estimated and controlled by the torque readings of a Cybex II dynamometer. It was found that the VMG and EMG under the same conditions on the same muscle group are in general equally sensitive to the levels of muscle contraction. Results show that the rms value of the VMG signal increases linearly, in a manner similar to the EMG rms/%MVC relationship, with increasing muscle contraction levels. Furthermore, the study indicates that the averaged MF (6-24 Hz) and PF (9-19 Hz) of the VMG signals are much lower than the MF (75-109 Hz) and PF (40-80 Hz) of the EMG signals. The slopes of MF/%MVC curves for the VMG and EMG are approximately the same for 60 degrees and 90 degrees joint angles (approximately 3.1 Hz per 20% MVC for VMG and approximately 2.6 Hz per 20% MVC for EMG).(ABSTRACT TRUNCATED AT 250 WORDS)     

基于DSP的肌电信号采集处理

肌电信号(Electromyography)EMG作为一种重要的生物电信号,已经被广泛应用于仿生学、生物反馈、运动医学和康复工程中。近年来,肌电信号的研究发展日益迅猛。本文主要研究了肌电信号的采集、分析,基于现有的数据采集放大电路和DSP开发平台,找出肌肉产生肌电信号较强的位置即最合适安放采集电极的位置。本文选择了九种手势动作,运用MATLAB对做不同手势动作时采集的肌电信号进行离线分析,同时对肌电信号进行滤波处理,分析得出结论。对采集过程中的干扰源进行分析,尽可能减少50Hz工频信号的干扰。     

Motor Unit Firing Rate During Static Contraction Indicated by the Surface EMG Power Spectrum

In this study, power spectral density functions (PSDF's) were computed of interference EMG of various facial and jaw-elevator muscles during nonfatiguing submaximal static contractions, recorded with surface electrodes. A distinct peak was found in the PSDF's in the frequency region below 40 Hz. It was shown that the peak was due to genuine EMG activity and that it could not be considered as an artifact, which was caused by electrode displacements during contraction. An increase of contraction strength resulted in a shift of the peak to higher frequencies and a decrease of peak amplitude relative to the power spectral estimates above 40 Hz, which were shown to be determined by the shape of the motor unit (MU) action potentials. In accordance with mathematical models of the EMG PSDF, it was demonstrated that the peak indicates the dominant firing rate of the sampled MU's. Our results suggest that this can be defined as the firing rate of the first recruited low-threshold MU's, which may be expected to dominate the interference EMG signal because of their preponderance in number. The data further suggest that the peak can be more readily observed in PSDF's of facial and jaw-elevator muscles than in PSDF's of limb muscles. This might be related to differences in MU firing statistics.     

Nonstationary harmonic modeling for ECG removal in surface EMG signals

We present a compact approach for mitigating the presence of electrocardiograms (ECG) in surface electromyographic (EMG) signals by means of time-variant harmonic modeling of the cardiac artifact. Heart rate and QRS complex variability, which often account for amplitude and frequency time variations of the ECG, are simultaneously captured by a set of third-order constant-coefficient polynomials modulating a stationary harmonic basis in the analysis window. Such a characterization allows us to significantly suppress ECG from the mixture by preserving most of the EMG signal content at low frequencies (less than 20?Hz). Moreover, the resulting model is linear in parameters and the least-squares solution to the corresponding linear system of equations efficiently provides model parameter estimates. The comparative results suggest that the proposed method outperforms two reference methods in terms of the EMG preservation at low frequencies.     

基于静电探测的模拟滤波器的研究

在静电探测输出信号特性分析的基础上,引入了静电探测信号检测电路,根据滤波性能指标,设计了适用于静电探测的低通滤波器,并对所设计的模拟低通滤波器进行了仿真验证.该滤波器在400Hz以内基本上没有衰减,延时基本上在1ms左右.当频率为500Hz时,电压衰减为85%左右,即衰减为1.41 dB,阻带衰减值大于30dB.所设计的模拟低通滤波器基本上满足了静电探测的设计需求指标.     

4GHz级频率综合器下单片低相位噪声CMOS电感电容压控振荡器的设计

基于0.18μm RF CMOS工艺,采用双端调谐结构实现了一种可应用于WLAN的二次变频收发机的压控振荡器.其输出频率范围可以覆盖收发机所需4.1～4.3GHz的频段,其最大调谐范围为500MHz.在距中心频率4.189GHz为4MHz处的相位噪声为-117dBc/Hz,500kHz处为-107dBc/Hz.输出信号抖动的均方根值为4.423ps,输出功率为-8.68dBm.     

A 60-Hz Harmonic Eliminator

A 60-Hz harmonic eliminator has been designed to remove dc and 60-Hz line frequency and its harmonics. By triggering a 1024-point sliding average on each cycle of the line voltage, the harmonics of 60 Hz and dc are averaged from the input. This average is then substracted from the input to eliminate these components, leaving the rest of the signal unaffected. The circuit is equivalent to 500 notch filters at every harmonic of 60 Hz with each notch about 48 dB deep and less than 2 Hz wide.     

Adaptive fuzzy impedance control of exoskeleton robots with electromyography-based convolutional neural networks for human intended trajectory estimation

Among various uses of exoskeleton robots, the rehabilitation of stroke patients is a more recent application. There is, however, considerable environmental uncertainty in such systems including uncertain robot dynamics, unwanted user reflexes, and, most importantly, uncertainty in user intended trajectory. Hence, it is challenging to develop transparent, stable, and wide-scale exoskeleton robots for rehabilitation. This paper proposes an adaptive fuzzy impedance controller (AFIC) and a convolutional neural network (CNN) which uses electromyographic (EMG) signals for early detection of human intention and better integration with a lower limb exoskeleton robot. Specifically, the primary purpose of the AFIC is to manage the mechanical interaction between human, robot, and environment and to deal with uncertainties in internal control parameters. CNN uses EMG signals, inertial measurement units, foot force sensing resistors, joint angular sensors, and load cells to deal with signal uncertainties and noise through automatic feature processing in order to detect user’s desired joint angles with high accuracy. EMG is particularly effective here since it reflects the human intention to move faster than the other mechanical sensors. In the experimental procedure, signals were sampled at 500 Hz as two healthy individuals walked normally at 0.3, 0.4, 0.5, and 0.6 m/s for eight minutes while wearing a robot with zero inertia. Approximately 70% of the data is used for training and 30% for testing the network. The estimated angle from the trained network is then used as the desired angle in the AFIC loop, which controls the robot online as the desired trajectory. Pearson correlation coefficient and normalized root mean square error are computed to evaluate the accuracy and robustness of the proposed angle estimation with CNN and AFIC algorithms. Experimental results show that the proposed approach successfully obtains the torque of the robot joints despite uncertainties in changing the walking speed.     

Independence of myoelectric control signals examined using a surface EMG model

The detection volume of the surface electromyographic (EMG) signal was explored using a finite-element model, to examine the feasibility of obtaining independent myoelectric control signals from regions of reinnervated muscle. The selectivity of the surface EMG signal was observed to decrease with increasing subcutaneous fat thickness. The results confirm that reducing the interelectrode distance or using double-differential electrodes can increase surface EMG selectivity in an inhomogeneous volume conductor. More focal control signals can be obtained, at the expense of increased variability, by using the mean square value, rather than the root mean square or average rectified value.     

Estimation of QRS complex power spectra for design of a QRS filter

We present power spectral analysis of ECG waveforms as well as isolated QRS complexes and episodes of noise and artifact. The power spectral analysis shows that the QRS complex could be separated from other interfering signals. A bandpass filter that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio would be of use in many ECG monitoring instruments. We calculate the coherence function and, from that, the signal-to-noise ratio. Upon carrying out this analysis on experimentaly obtained ECG data, we observe that a bandpass filter with a center frequency of 17 Hz and a Q of 5 yields the best signal-to-noise ratio.     

Frequency- and time-domain FEM models of EMG: capacitive effects and aspects of dispersion

Electromyography (EMG) simulations have traditionally been based on purely resistive models, in which capacitive effects are assumed to be negligible. Recent experimental studies suggest these assumptions may not be valid for muscle tissue. Furthermore, both muscle conductivity and permittivity are frequency-dependent (dispersive). In this paper, frequency-domain and time-domain finite-element models are used to examine the impact of capacitive effects and dispersion on the surface potential of a volume conductor. The results indicate that the effect of muscle capacitance and dispersion varies dramatically. Choosing low conductivity and high permittivity values in the range of experimentally reported data for muscle can cause displacement currents that are larger than conduction currents with corresponding reduction in surface potential of up to 50% at 100 Hz. Conductivity and permittivity values lying toward the middle of the reported range yield results which do not differ notably from purely resistive models. Also, excluding dispersion can also cause large error-up to 75% in the high frequency range of the EMG. It is clear that there is a need to establish accurate values of both conductivity and permittivity for human muscle tissue in vivo in order to quantify the influence of capacitance and dispersion on the EMG signal.     

Compact optical millimetre-wave source using a dual-modesemiconductor laser

The authors demonstrate the optical generation of extremely narrow linewidth millimetre-wave signals between 40 and 60 GHz using a single-chip semiconductor laser. A dual-mode long-cavity multisection DFB semiconductor laser is driven at a subharmonic of the free-running-mode beat signal frequency to produce phase-locked millimetre waves with a 3 dB linewidth of less than 10 Hz and a 3 dB locking range of ~500 MHz     

Spectra of Auditory Brainstem Responses and Spontaneous EEG

Human auditory brainstem responses (ABR's) are sensory evoked potentials that can be recorded within a few milliseconds following a transient acoustic stimulus. This paper describes results of a simulation study that evaluated alternative techniques for estimating the spectrum of the ABR signal and of the background EEG. The effects of residual noise in the average signal, the number of responses in the average, and the use of smoothing windows were considered. Spectra of human ABR's were also obtained. The spectral energy decreases with frequency to 2 kHz and seems to flatten above 2 kHz. The ABR signal spectrum is limited to below approximately 1.5 kHz. There are three main components: a low-frequency component around 100 Hz, a midfrequency component around 500 Hz, and a high-frequency component at 1000 Hz. Narrow-band filtering suggests that the midfrequency component is primarily associated with Jewett wave V, while the high-frequency component is primarily associated with the early Jewett waves. These two components are probably the most important for determining the latencies of waves. The low-frequency component appears to correspond to a slow wave on which the Jewett waves are superimposed.     

Analysis and comparison of reflex times and electromyograms of cervical muscles under impact loading using surface and fine-wire electroces

Myoelectric signals [electromyograms (EMGs)] can be collected using either surface or fine-wire electrodes. Application of the latter results in higher-frequency contents of EMG. In the field of impact biomechanics, surface electrodes are more often utilized than fine-wire ones. However, the removal of motion artefacts from EMG recorded under transient loads requires application of high-pass filters with relatively high cutoff frequencies, which may eliminate a significant part of the surface EMG power spectra. Therefore, in the current study, both surface and fine-wire electrodes were utilized to record the EMG of cervical muscles under conditions simulating a rear-end car collision at low speed. The results indicated that application of high-pass filtering at 50 Hz can be necessary to remove motion artefacts from the EMG collected under such conditions. Such filtering resulted in a higher decrease in amplitude of the surface EMG than that of the fine-wire one. However, the reflex times obtained here were not significantly affected by the type of the electrodes utilized to collect EMG.     

Micropower Pulse Frequency Modulator for Use in an Optically Isolated Catheter System

A micropower pulse frequency modulator (PFM) has been developed for implementing an opticaily isolated system capable of making sensor voltage measurements. The PFM circuit is powered with an array of solar cells which converts light energy into electrical energy. An input port receives the sensor voltage. The PFM signal generated by the circuit drives an infrared emitting diode which provides the frequency-modulated output signal. The PFM circuit can accept sensor output voltages in the range ±500 mV and has a minimum bandwidth of 500Hz.     

A multiple-layer finite-element model of the surface EMG signal

The effect of skin, muscle, fat, and bone tissue on simulated surface electromyographic (EMG) signals was examined using a finite-element model. The amplitude and frequency content of the surface potential were observed to increase when the outer layer of a homogeneous muscle model was replaced with highly resistive skin or fat tissue. The rate at which the surface potential decreased as the fiber was moved deeper within the muscle also increased. Similarly, the rate at which the surface potential decayed around the surface of the model, for a constant fiber depth, increased. When layers of subcutaneous fat of increasing thickness were then added to the model, EMG amplitude, frequency content, and the rate of decay of the surface EMG signal around the limb decreased, due to the increased distance between the electrodes and the active fiber. The influence of bone on the surface potential was observed to vary considerably, depending on its location. When located close to the surface of the volume conductor, the surface EMG signal between the bone and the source and directly over the bone increased, accompanied by a slight decrease on the side of the bone distal to the active fiber. The results emphasize the importance of distinguishing between the effects of material properties and the distance between source and electrode when considering the influence of subcutaneous tissue, and suggest possible distortions in the surface EMG signal in regions where a bone is located close to the skin surface.     

Evaluation of the Effectiveness of EMG Parameters in the Study of Neurogenic Diseases-A Statistical Approach Using Clinical and Simulated Data

Of interest here is the problem of determining to what extent combinations of parameters derived from the EMG signal allow 1) discriminating two subclasses of neurogenic myopathies, and 2) recognizing different morphologies of the motor unit action potential underlying a measured EMG signal. EMG signals measured on clinical subjects and computer-simulated EMG signals were collected in a database and used cooperatively in this study. Suitable statistical models were developed which allow testing hypotheses on the role of accepted EMG parameters for the two purposes named above, and deriving new suitable combinations of EMG parameters. Results support the hypothesis that frequency-domain parameters are very clearly related to the morphology of the motor unit action potential. However, the attempt to use them in order to discriminate the two pathologic subclasses considered appears to be jeopardized by the fact that the signal may be measured in territories which do not reflect the morphology of the motor unit action potential dominant in such subclasses. On the basis of time-domain parameters, a significant discrimination was obtained between the two subclasses, and such discrimination is related mainly to a time-domain parameter which has already proved successful in the discrimination between myopathic and normal subjects. Data corroborate the hypothesis that the diagnostic yield improves when time-domain EMG parameters are measured at recruitment.     

A 2.1 μW 80 dB SNR DT ΔΣ modulator for medical implant devices in 65 nm CMOS

This paper presents a simple and robust low-power ΔΣ modulator for accurate ADCs in implantable cardiac rhythm management devices such as pacemakers. Taking advantage of the very low signal bandwidth of 500 Hz which enables high oversampling ratio, the objective is to obtain high SNDR and low power consumption, while limiting the complexity of the modulator to a second-order architecture. Significant power reduction is achieved by utilizing a two-stage load-compensated OTA as well as the low-V_T devices in analog circuits and switches, allowing the modulator to operate at 0.9 V supply. Fabricated in a 65 nm CMOS technology, the modulator achieves 80 dB peak SNR and 76 dB peak SNDR over a 500 Hz signal bandwidth. With a power consumption of 2.1 μW, the modulator obtains 0.4 pJ/step FOM. To the authors’ knowledge, this is the lowest reported FOM, compared to the previously reported second-order modulators for such low-speed applications. The achieved FOM is also comparable to the best reported results from the higher-order ΔΣ modulators.     

Cross-gain modulation in Raman fiber amplifier: experimentation and modeling

The effects of channel loss on the performance of Raman fiber amplifiers (RFA) are investigated both experimentally and theoretically. Signals from one distributed-feedback (DFB) laser and an external cavity laser (ECL) were transmitted through counterdirectionally pumped RFAs consisting of 15.6 km of dispersion compensating fiber (DCF). The ECL light was square-wave modulated at 500 Hz. At the output of the RFA, the signal of the modulated channel was eliminated with an optical band pass filter, and power fluctuations of the surviving channel were recorded with a high-speed digital oscilloscope. Power fluctuations as high as 0.45 dB with typical saturated amplifier overshoots were observed. The experimental results were confirmed by a large signal numerical analysis.     

A single-lead ECG enhancement algorithm using a regularized data-driven filter

We presented a novel way of deriving a subspace filter for enhancing a noisy electrocardiogram (ECG) signal contaminated by electromyogram (EMG). The new subspace filter was based on a multiple cycle prediction (MCP) modeling of a single-lead ECG. The adoption of an MCP model resulted in a data matrix more suitable for separating noise and signal subspaces than the linear prediction (LP) model that is implicitly assumed in many existing subspace filters. Alignment of ECG cycles of different length is required for MCP modeling and was handled by a dynamic time warping (DTW) algorithm. A run-time procedure was designed for automatically determining the signal space dimension adaptively. To validate the new filter in a quantitative way, 12 clean realistic ECG segments with different degrees of heart rate variability generated using the ECGSyn program were mixed with different realizations of EMG noise in the MIT-BIH Noise Stress Test Database and locally acquired EMG at a typical 10-dB signal-to-noise ratio. The performance of the proposed method was compared to three existing ECG enhancement algorithms and achieved encouraging results. In addition, various ECG recordings from MIT-Arrythmia database were also mixed with EMG noise and subjected to the same four filters resulting in a qualitative comparison of them.