Estimation Of Muscle Fiber Conduction Velocity Research Articles

Influence of Electrode Configuration on Muscle-Fiber-Conduction-Velocity Estimation Using Surface Electromyography.

Muscle fiber conduction velocity (MFCV) is an important myoelectric parameter and can be estimated by analysing surface electromyography (EMG). Among many factors, electrode configuration plays a key role on MFCV estimation. Most studies adopt bipolar configuration (BC) for CV estimation. However, a thorough understanding of the underlying mechanism is lacking, confusing the design of the most appropriate EMG measurement setup for CV estimation. The aim of this study is therefore to systematically investigate the influence of electrode configuration on MFCV estimation. Four possible configurations are considered, including BC, monopolar configuration (MC), common average reference (CAR), and a special monopolar configuration (SMC) using a fixed channel on the active muscle as reference. For each configuration, mathematical models computing the time delay between adjacent channels are derived and evaluated by dedicated simulation as well as real EMG measurements. MFCV was calculated using the maximum likelihood algorithm with and without channel normalization. The simulation results are in line with the mathematical models. The CVs estimated from the real EMG with and without normalization are 4.3 ±0.7 and 7.2 ±3.7 m/s, 5.7 ±1.3 and 20.4 ±4.7 m/s, 9.0 ±3.4 and 20.6 ±9.8 m/s, and 5.5 ±2.5 and 5.5 ±2.4 m/s for BC, MC, SMC, and CAR, respectively. Our results show normalized BC to produce the most accurate CV estimation, in line with the mathematical models and the simulation results. These findings enable a better understanding of the influence of electrode configuration on MFCV estimation, providing useful information for EMG measurement setup design aiming at MFCV studies.

Towards Real-Time Estimation of Muscle-Fiber Conduction Velocity Using Delay-Locked Loop

Decrease in muscle-fiber conduction velocity (MFCV) during sustained contraction has been widely accepted as myoelectric manifestation of muscle fatigue. Several methods have been proposed in the literature for MFCV estimation by analysing surface electromyography (EMG), e.g., cross-correlation (CC) function and maximum likelihood (ML). However, for all the availablemethods, windowing of the EMG signal and computationally demanding calculations are required, limiting the possibility to continuously monitor muscle fatigue in real time. In the present study, an adaptive scheme is proposed that permits real-time estimation of MFCV. The proposed scheme is based on a delay-lockedloop (DLL). Asecond-orderloop is adopted to track the delay variationover time. An error filter is employed to approximate a ML estimation in case of colored noise. Furthermore, the DLL system is extended for multichannel CV estimation. The performance of the proposed method is evaluated by both dedicated simulations and real EMG signals. Our results show the accuracy of the proposed method to be comparable to that of theML method formuch lower (1/40) computational complexity, especially suited for real-time MFCV measurements. Use of this method can enable new studies onmyoelectric fatigue, possibly leading to new insight on the underlying physiological processes.

Muscle fiber conduction velocity estimation by the multi‐dip method: How deep can you dip?

Muscle fiber conduction velocity estimation by the multi‐dip method: How deep can you dip?

Reply: Muscle fiber conduction velocity estimation by the multi‐dip method; how deep can you dip?

Reply: Muscle fiber conduction velocity estimation by the multi‐dip method; how deep can you dip?

Muscle fiber conduction abnormalities in early diabetic polyneuropathy

Objective Diabetic polyneuropathy (PNP) has been proposed to be a primary disorder of sensory nerves. At an early stage motor nerve conduction velocity (MNCV) and muscle strength remain preserved due to compensatory mechanisms (axonal sprouting, reinnervation). We evaluated the use of invasive muscle fiber conduction velocity (MFCV) measurements as a method to detect muscle fiber denervation atrophy, as an early sign of motor axonal loss in diabetes mellitus (DM). Methods Twelve selected male patients (8 type 1, 4 type 2; mean age 35.8 years, SD 10.6), without any sign of micro- or macroangiopathy, were studied by systematic clinical and neurophysiological testing including MFCV estimation. Results Hand-held dynamometry was normal in all subjects. There were no signs of recent denervation by concentric needle EMG in any of the patients. Sensory nerve conduction velocity (SNCV) was abnormal in 6 subjects, MFCV in 6 subjects (5 had also low SNCV). The ratio of fastest/slowest muscle fibers in MFCV was correlated to SNCV of sural nerve (−.59, p < .05), but not to MNCV. Conclusions Half of the clinically asymptomatic DM subjects showed sensory involvement together with MFCV abnormalities, despite normal needle EMG and force. Significance MFCV estimation offers a sensitive method in detecting early signs of motor axonal dysfunction in DM.

Response of Electromyographic Variables during Incremental and Fatiguing Cycling

Surface electromyographic (EMG) variables allow study of the electrical activity of motor units within active skeletal muscle. Recent methodological advances permit the estimation of muscle fiber-conduction velocity (MFCV) from EMG signals recorded during dynamic exercise. However, EMG responses to incremental and fatiguing cycling are poorly understood. Twenty males completed an incremental cycling protocol (20, 40, 60, and 80% of peak power output (PPO)) on two occasions separated by 5 d. The final stage was 6 min long, to induce muscle fatigue. EMG signals were recorded from the vastus lateralis and vastus medialis muscles, and root mean square (RMS) and MFCV values were estimated. For incremental exercise, EMG data were reliable between trials, especially for MFCV in the vastus lateralis at 40, 60, and 80% PPO (e.g., coefficient of variation < 6.3%; bias < 0.4 m.s (-1); limits of agreement < 1.2 m.s(-1)). Significant positive correlations were observed between RMS and MFCV (r = 0.79-0.83), as both measures increased with power output (repeated-measures analysis of variance all P = 0.001). For fatiguing exercise, many EMG variables showed systematic bias between trials. RMS (P = 0.009 to 0.051) but not MFCV (P = 0.236-0.578) significantly increased during fatiguing cycling. During dynamic cycle exercise, EMG variables are reliable and increase with power output. During fatiguing exercise, EMG variables may be sensitive to learning effects in the execution of the task. MFCV and RMS are correlated with varying power output in the nonfatigued muscle, but not during the development of fatigue.

Correlation of average muscle fiber conduction velocity measured during cycling exercise with myosin heavy chain composition, lactate threshold, and VO 2max

The aim of the study was to investigate the correlation between myosin heavy chain (MHC) composition, lactate threshold (LT), maximal oxygen uptake VO 2max, 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 VO 2max, 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 VO 2max were correlated with percentage of MHC I ( R 2 = 0.77; and 0.42, respectively; P < 0.05). MFCV was positively correlated with percentage of MHC I, power corresponding to LT and to VO 2max ( R 2 = 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.

Muscle fiber conduction velocity estimation by using normalized peak-averaging technique

In order to compute the muscle fiber conduction velocity (MFCV) and to clarify how action potentials are conducted, the normalized peak-averaging technique (NPAT) was newly employed together with computer softwares. Twelve pairs of surface electromyograms were selected from biceps brachii muscles during contraction at a level of 50% of the maximum voluntary isometric contraction in seven healthy volunteers. The techniques to compute MFCV from the time delay of the peaks (P-NPAT) and from the cross correlation (CC-NPAT) of averaged pulses were compared to the cross-correlation technique (CCT). The spread rate of averaged pulses was computed to estimate the spread of MFCVs in different motor units. Tri-phasic averaged pulses were obtained clearly by averaging more than 500 detected pulses. The P-NPAT and CC-NPAT highly correlated with the CCT in the computed MFCVs. The MFCVs obtained by P-NPAT were generally larger than those obtained by CCT, and the spread rates had in the definite values. These results suggest that the MFCV could be computed and the spread of MFCVs would be estimated from averaged pulses. The MFCV of a patient with myotonic dystrophy was also studied, and it was suggested that the NPAT would be clinically useful.

Comparison of single and multiple time delay estimators: application to muscle fiber conduction velocity estimation

The problem of estimating the average conduction velocity (CV) in muscle fibers is considered. The velocity is estimated from an array of noisy deterministic and unknown motor unit action potentials (MUAP) acquired by an array of K co-linear electrodes. In this array a vector of ( K−1) independent time delays (TD) are to be estimated. Three velocity estimates are deduced from TD estimates: a maximum likelihood estimation (MLE) to estimate a constant velocity and two suboptimal estimates for a spatially varying velocity are presented. The suboptimal methods consist of a postcorrelation processing in which independent linear relations between TDs in the array (Chasles relations) are exploited. From the correlation step K( K−1)/2 possible TDs are estimated (coarse estimate) followed by a refinement of these estimates subject to the linear constraints to obtain the fine estimates. By the manner of introducing the constraints we distinguish the two suboptimal methods. The CV harmonic mean is finally calculated from the fine TD estimates. The Cramér–Rao lower bound (CRLB) for the TDs and CV are derived. It is shown that the CRLB of the TD estimates is at least two times higher than the case where the signal is known. Also shown that the CRLB of the velocity estimate is lower for large interelectrode distances. Numerical results show that velocity estimates are always positively biased and the smaller the estimated TD, the higher the velocity bias. The effect of an increased number of signals in the array is shown to decrease the velocity variance. Finally, the proposed methods are applied to arrays of real SEMG signals and give velocities in the range of 4.4– 5.9 m/ s .

Effect of muscle length on distribution of muscle fiber conduction velocity for M. biceps brachii.

The influence of the muscle length on the distribution of muscle fiber conduction velocity (MFCV) was estimated at 3 different elbow angles (90 degrees, 120 degrees and 150 degrees) during the contraction of 30% of isometric maximum voluntary contraction (MVC). Surface electromyogram was recorded in the distal region of m. biceps brachii using a surface array electrode. MFCVs at the different locations from the end-plate to the distal tendon of the muscle were measured directly using the averaging method. MFCVs near the end-plate and the distal tendon of the muscle showed the high values, while MFCVs in the middle region between the end-plate and the distal tendon of the muscle had low values at the same elbow angles. The mean MFCVs in the middle region decreased when the muscle length increased during the contraction of 30% MVC. The middle region length changed when the elbow joint was extended from the elbow angle of 90 degrees to that of 150 degrees. Although previous studies showed the influence of muscle length on MFCV for special regions (like end-plate, tendon, and middle region), MFCV for whole regions, that is, the distribution of MFCV has not been studied for various muscle length. The MFCV for the same region in the some muscle length could not be estimated. In the study, the distribution of MFCV in various muscle length for the contraction of 30% MVC was obtained, and the model with the function including muscle length for the estimation of MFCV was presented.

Measurement of muscle fiber conduction velocity in humans: techniques and applications.

Different methods of measuring human muscle fiber conduction velocity (MFCV) are outlined. A major division can be made between invasive (needle) and noninvasive [surface electromyographic (EMG)] recording techniques. The benefits and limitations associated with the different approaches are discussed. The conduction velocity of individual muscle fibers are measurable with needle recordings on routine EMG apparatus. Velocities measured with electrical stimulation of single fibers or bundle of fibers are in general lower than recordings made during voluntary contraction. With surface EMG, an average estimate of the MFCV from many fibers and motor units can be determined with computerized data analyses--both frequency and time domain approaches. The cross-correlation method is widely used to calculate the time lag between two EMGs recorded along the muscle. Using the surface EMG-based methods, MFCV can be calculated at all contraction levels. Thus, the change in sarcolemmal function during local muscle fatigue can be assessed. Physiological factors influencing the MFCV and the utility of the MFCV estimation in clinical practice are discussed. Compared to nerve conduction velocity studies, the clinical interest in MFCV has been limited.