Advances in processing of surface myoelectric signals: Part 2

Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

Power spectrum of the rectified EMG: when and why is rectification beneficial for identifying neural connectivity?

OBJECTIVE

The identification of common oscillatory inputs to motor neurons in the electromyographic (EMG) signal power spectrum is often preceded by EMG rectification for enhancing the low-frequency oscillatory components. However, rectification is a nonlinear operator and its influence on the EMG signal spectrum is not fully understood. In this study, we aim at determining when EMG rectification is beneficial in the study of oscillatory inputs to motor neurons.

APPROACH

We provide a full mathematical description of the power spectrum of the rectified EMG signal and the influence of the average shape of the motor unit action potentials on it. We also provide a validation of these theoretical results with both simulated and experimental EMG signals.

MAIN RESULTS

Simulations using an advanced computational model and experimental results demonstrated the accuracy of the theoretical derivations on the effect of rectification on the EMG spectrum. These derivations proved that rectification is beneficial when assessing the strength of low-frequency (delta and alpha bands) common synaptic inputs to the motor neurons, when the duration of the action potentials is short, and when the level of cancellation is relatively low. On the other hand, rectification may distort the estimation of common synaptic inputs when studying higher frequencies (beta and gamma), in a way dependent on the duration of the action potentials, and may introduce peaks in the coherence function that do not correspond to physiological shared inputs.

SIGNIFICANCE

This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.

Repeatability of electrically-evoked myoelectric signals in the human tibialis anterior muscle

The reproducibility of surface myoelectric signal measurements is of paramount importance for clinical applications of electromyography (EMG) techniques. The repeatability of electrically-evoked myoelectric signal shape (M-wave) as well as spectral and amplitude parameters, conduction velocity and elicited torque was tested, in isometric conditions, on the tibialis anterior muscle of 10 normal subjects. Contractions were elicited by stimulation of the main muscle motor point and repeated after removal and replacement of the stimulation and detection electrodes in the same carefully marked locations. This protocol was repeated five times on each subject on five different days. The test-retest Pearson correlation coefficient, the paired t test and analysis of variance (ANOVA) were used to quantify repeatability and estimate the fraction of variance due to repeated trials within experiments, repeated experiments within subjects and inter-subject variability. Results indicate that parameters of spectral variables are more repeatable than those of amplitude variables. Elicited torque and conduction velocity show the lowest repeatability. The intra-class correlation coefficient ranged from 87.9% for the initial value of median frequency to 11.5% for the initial value of conduction velocity. Fatigue indices based on the time course of the myoelectric signal variables showed even lower values of this coefficient. It is concluded that: (a) initial values and fatigue indices based on spectral variables are more repeatable than those based on amplitude variables; (b) the repeatability of conduction velocity and torque is very poor; (c) M-wave shape, rather than amplitude or width, seems to be a characteristic of individual muscles; and (d) electrode location is a critical issue in the study of M-waves elicited by stimulation of a muscle motor point. The methodology for estimation of muscle fibre conduction velocity must be refined and the characterization of evoked responses must be improved to allow widespread clinical applications.

Surface electromyography features in manual workers affected by carpal tunnel syndrome

INTRODUCTION

Alterations in surface electromyographic (sEMG) signals of the abductor pollicis brevis muscle were evaluated in 24 non-manual workers and 40 manual workers (25 asymptomatic and 15 reporting CTS symptoms).

METHODS

The initial value (IV) and the normalized rate of change (NRC) of average rectified value (ARV), mean frequency of the power spectrum (MNF), and muscle fiber conduction velocity (MFCV) were calculated during contractions at 20% and 50% of maximal voluntary contraction (MVC). Neuromuscular efficiency (NME) and kurtosis of the sEMG amplitude distribution were estimated.

RESULTS

With respect to controls, manual workers showed higher NME, lower ARV IV, and reduced myoelectric manifestations of fatigue (lower MNF NRC for both contraction levels, and lower MFCV NRC at 50% MVC). Kurtosis at 20% MVC showed higher values in symptomatic manual workers than in the other two groups.

CONCLUSIONS

Kurtosis seems to be a promising parameter for use in monitoring individuals who develop CTS.

A new LMS algorithm for analysis of atrial fibrillation signals

BACKGROUND

A biomedical signal can be defined by its extrinsic features (x-axis and y-axis shift and scale) and intrinsic features (shape after normalization of extrinsic features). In this study, an LMS algorithm utilizing the method of differential steepest descent is developed, and is tested by normalization of extrinsic features in complex fractionated atrial electrograms (CFAE).

METHOD

Equations for normalization of x-axis and y-axis shift and scale are first derived. The algorithm is implemented for real-time analysis of CFAE acquired during atrial fibrillation (AF). Data was acquired at a 977 Hz sampling rate from 10 paroxysmal and 10 persistent AF patients undergoing clinical electrophysiologic study and catheter ablation therapy. Over 24 trials, normalization characteristics using the new algorithm with four weights were compared to the Widrow-Hoff LMS algorithm with four tapped delays. The time for convergence, and the mean squared error (MSE) after convergence, were compared. The new LMS algorithm was also applied to lead aVF of the electrocardiogram in one patient with longstanding persistent AF, to enhance the F wave and to monitor extrinsic changes in signal shape. The average waveform over a 25 s interval was used as a prototypical reference signal for matching with the aVF lead.

RESULTS

Based on the derivation equations, the y-shift and y-scale adjustments of the new LMS algorithm were shown to be equivalent to the scalar form of the Widrow-Hoff LMS algorithm. For x-shift and x-scale adjustments, rather than implementing a long tapped delay as in Widrow-Hoff LMS, the new method uses only two weights. After convergence, the MSE for matching paroxysmal CFAE averaged 0.46 ± 0.49 μV(2)/sample for the new LMS algorithm versus 0.72 ± 0.35 μV(2)/sample for Widrow-Hoff LMS. The MSE for matching persistent CFAE averaged 0.55 ± 0.95 μV(2)/sample for the new LMS algorithm versus 0.62 ± 0.55 μV(2)/sample for Widrow-Hoff LMS. There were no significant differences in estimation error for paroxysmal versus persistent data. From all trials, the mean convergence time was approximately 1 second for both algorithms. The new LMS algorithm was useful to enhance the electrocardiogram F wave by subtraction of an adaptively weighted prototypical reference signal from the aVF lead. The extrinsic weighting over 25 s demonstrated that time-varying functions such as patient respiration could be identified and monitored.

CONCLUSIONS

A new LMS algorithm was derived and used for normalization of the extrinsic features in CFAE and for electrocardiogram monitoring. The weighting at convergence provides an estimate of the degree of similarity between two signals in terms of x-axis and y-axis shift and scale. The algorithm is computationally efficient with low estimation error. Based on the results, proposed applications include monitoring of extrinsic and intrinsic features of repetitive patterns in CFAE, enhancement of the electrocardiogram F wave and monitoring of time-varying signal properties, and to quantitatively characterize mechanistic differences in paroxysmal versus persistent AF.

The effects of notch filtering on electrically evoked myoelectric signals and associated motor unit index estimates

Background

Notch filtering is the most commonly used technique for suppression of power line and harmonic interference that often contaminate surface electromyogram (EMG) signals. Notch filters are routinely included in EMG recording instrumentation, and are used very often during clinical recording sessions. The objective of this study was to quantitatively assess the effects of notch filtering on electrically evoked myoelectric signals and on the related motor unit index measurements.

Methods

The study was primarily based on an experimental comparison of M wave recordings and index estimates of motor unit number and size, with the notch filter function of the EMG machine (Sierra Wave EMG system, Cadwell Lab Inc, Kennewick, WA, USA) turned on and off, respectively. The comparison was implemented in the first dorsal interosseous (FDI) muscle from the dominant hand of 15 neurologically intact subjects and bilaterally in 15 hemiparetic stroke subjects.

Results

On average, for intact subjects, the maximum M wave amplitude and the motor unit number index (MUNIX) estimate were reduced by approximately 22% and 18%, respectively, with application of the built-in notch filter function in the EMG machine. This trend held true when examining the paretic and contralateral muscles of the stroke subjects. With the notch filter on vs. off, across stroke subjects, we observed a significant decrease in both maximum M wave amplitude and MUNIX values in the paretic muscles, as compared with the contralateral muscles. However, similar reduction ratios were obtained for both maximum M wave amplitude and MUNIX estimate. Across muscles of both intact and stroke subjects, it was observed that notch filtering does not have significant effects on motor unit size index (MUSIX) estimate. No significant difference was found in MUSIX values between the paretic and contralateral muscles of the stroke subjects.

Conclusions

The notch filter function built in the EMG machine may significantly reduce the M wave amplitude and the MUNIX measurement. However, the notch filtering does not jeopardize the evaluation of the reduction ratio in maximum M wave amplitude and MUNIX estimate of the paretic muscles of stroke subjects when compared with the contralateral muscles.

Experimental muscle pain decreases voluntary EMG activity but does not affect the muscle potential evoked by transcutaneous electrical stimulation

OBJECTIVE

The aim of this human study was to investigate if voluntary EMG activity and supra-maximal M-wave are affected by injection of hypertonic saline to experimentally induce muscle pain.

METHODS

Surface EMG signals were recorded with an electrode array from the tibialis anterior muscle of 12 subjects. Two sets of 6 contractions, 3 electrically elicited and 3 voluntary (30% of the maximal force), alternated, were performed with each leg. During the second set of 6 contractions, hypertonic (painful; right leg) or isotonic (non-painful; left leg) saline was injected 3 times (0.2, 0.5, 0.9 ml), separated by 140 s, into the tibialis anterior.

RESULTS

In the voluntary contractions, EMG average rectified value (ARV) significantly decreased (mean+/-SE, 13.2 +/- 4.2%) with increasing pain, although the exerted torque was unaltered. Conduction velocity (CV) (4.2 +/- 0.2 and 4.4 +/- 0.3 m/s, right and left leg, respectively) and mean power spectral frequency (MPF) (119.0 +/- 8.4 and 119.5+/-8.9 Hz) were not affected by the injection of hypertonic saline. In the electrically elicited contractions, M-wave CV (4.6 +/- 0.3 and 4.7 +/- 0.2 m/s), ARV (748.6 +/- 101.8 and 822.3 +/- 104.4 microV), and MPF (72.0+/-5.1 and 76.9+/-4.8 Hz) did not change with pain.

CONCLUSIONS

Injection of hypertonic saline did not change muscle fiber conduction velocity or impaire neuromuscular transmission. The decrease in voluntary EMG activity with injection of hypertonic saline was thus due to central factors.

SIGNIFICANCE

The injection of hypertonic saline provides a model for exciting nociceptive afferents without affecting muscle fiber electrophysiological properties.

Myoelectric manifestations of fatigue during exposure to hypobaric hypoxia for 12 days

Lack of oxygen, as occurs at high altitude (HA), leads to a number of adaptive processes in muscle, but their precise nature is unclear. To better understand mechanisms of adaptations of the neuromuscular system to HA, we collected surface electromyographic (EMG) signals during a 12-day stay at 5,050 m above sea level (SL). The aim was to investigate the effect of hypobaric hypoxia on muscle-fiber membrane and motor-unit control properties. Surface EMG signals were recorded from the dominant biceps brachii muscle of six subjects at HA and 3 months after their return to SL. Supramaximal electrical stimuli (25 HZ) were delivered and voluntary isometric contractions at 40 and 80% of maximal voluntary torque were performed in 10 experimental sessions at HA and in 3 at SL. Maximal isometric torque was not altered at HA. Surface EMG spectral frequencies at the beginning of the voluntary contractions were greater at HA than SL. The rates of change of spectral frequencies and conduction velocity during the voluntary contractions were significantly larger at HA than SL. No differences in EMG variables were observed in the electrically elicited contractions. The maximal torque and surface EMG variables did not depend on the day of measure at HA. It was concluded that acute exposure to hypobaric hypoxia does not significantly affect the muscle-fiber membrane properties but does impact motor-unit control properties. This provides new insights in the understanding of motor control in extreme conditions of oxygen reduction, with relevance for sport and rehabilitation medicine, and may also explain the pathophysiological adaptations of the neuromuscular system occurring in such disorders as chronic obstructive pulmonary disease.

Methods for estimating muscle fibre conduction velocity from surface electromyographic signals

The review focuses on the methods currently available for estimating muscle fibre conduction velocity (CV) from surface electromyographic (EMG) signals. The basic concepts behind the issue of estimating CV from EMG signals are discussed. As the action potentials detected at the skin surface along the muscle fibres are, in practice, not equal in shape, the estimation of the delay of propagation (and thus of CV) is not a trivial task. Indeed, a strictly unique definition of delay does not apply in these cases. Methods for estimating CV can thus be seen as corresponding to specific definitions of the delay of propagation between signals of unequal shape. The most commonly used methods for CV estimation are then reviewed. Together with classic methods, recent approaches are presented. The techniques are described with common notations to underline their relationships and to highlight when an approach is a generalisation of a previous one or when it is based on new concepts. The review identifies the difficulties of CV estimation and underlines the issues that should be considered by the investigator when selecting a particular method and detection system for assessing muscle fibre CV. The many open issues in CV estimation are also presented.

Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals

The study of surface electromyographic (EMG) signals under dynamic contractions is becoming increasingly important. However, knowledge of the methodological issues that may affect such analysis is still limited. The aim of the study was to analyse the effect of fibre shortening on estimates of conduction velocity (CV) and mean power spectral frequency (MNF) from surface EMG signals. Single fibre action potentials were simulated, as detected by commonly used spatial filters, for different fibre lengths. No physiological modifications were included with changes in fibre length, and thus only geometrical artifacts related to fibre shortening were investigated. The simulation results showed that the dependence of CV and MNF on fibre shortening is affected by the fibre location, electrode position and the spatial filter applied. With shortening of up to 50% for a fibre of 50 mm semi-length, the variations in CV and MNF estimates with shortening in bipolar recordings were 0.5% (CV) and 0.7% (MNF) for superficial fibres, and 3.6% and 5.1% for deeper fibres. Using the longitudinal double differential filter, under the same conditions, the percent variation was 0% and 0.2%, and 24.7% and 15.8%, respectively. The main conclusions were, first, muscle fibre shortening can significantly affect estimates of CV and MNF, especially for short fibre lengths. However, for long (semi-length >50 mm) and superficial fibres, this effect is limited for shortenings of up to 50% of the initial fibre length. Secondly, CV and MNF are almost equally affected by changes in muscle length; and, thirdly, sensitivity to fibre shortening depends on the spatial filter applied for signal detection.

A novel approach for estimating muscle fiber conduction velocity by spatial and temporal filtering of surface emg signals

We describe a new method for the estimation of muscle fiber conduction velocity (CV) from surface electromyography (EMG) signals. The method is based on the detection of two surface EMG signals with different spatial filters and on the compensation of the spatial filtering operations by two temporal filters (with CV as unknown parameter) applied to the signals. The transfer functions of the two spatial filters may have different magnitudes and phases, thus the detected signals have not necessarily the same shape. The two signals are first spatially and then temporally filtered and are ideally equal when the CV value selected as a parameter in the temporal filters corresponds to the velocity of propagation of the detected action potentials. This approach is the generalization of the classic spectral matching technique. A theoretical derivation of the method is provided together with its fast implementation by an iterative method based on the Newton's method. Moreover, the lowest CV estimate among those obtained by a number of filter pairs is selected to reduce the CV bias due to nonpropagating signal components. Simulation results indicate that the method described is less sensitive than the classic spectral matching approach to the presence of nonpropagating signals and that the two methods have similar standard deviation of estimation in the presence of additive, white, Gaussian noise. Finally, experimental signals have been collected from the biceps brachii muscle of ten healthy male subjects with an adhesive linear array of eight electrodes. The CV estimates depended on the electrode location with positive bias for the estimates from electrodes close to the innervation or tendon regions, as expected. The proposed method led to significantly lower bias than the spectral matching method in the experimental conditions, confirming the simulation results.

Simulation analysis of the ability to estimate motor unit propagation velocity non-invasively by different two-channel methods and types of multi-electrodes

Ability to estimate motor unit propagation velocity correctly using different two-channel methods for delay estimation and different non-invasive spatial filters was analysed by simulation. It was established that longitudinal double difference electrodes could be not a better choice than simple bipolar parallel electrodes. Spatial filtration with a new multi-electrode (performing difference between signals detected by two transversal double difference electrodes positioned along the muscle fibres) promises to give the best estimate. Delay estimation between reference points is more preferable than that based on the cross-correlation technique, which is considerably sensitive to the fundamental properties of the muscle fibre extracellular fields. Preliminary averaging and approximation of the appropriate parts of the signals around chosen reference points could reduce the larger noise sensitivity and the effects of local tissue inhomogeneities as well as eliminate the sampling problem. A correct estimate of the propagation velocity could be impossible, even in the case of not very deep motor units (15 or 10 mm, depending on the spatial filter used) with relatively long (about 120 mm) muscle fibres. In the case of fibres with asymmetrical location of the end-plates in respect to the fibre ends, the propagation velocity estimates could be additionally biased above the longer semilength of the motor unit fibres.

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 m s(-1) and 0.79 m s(-1) for the peak method, with and without interpolation, respectively, and 0.50 m s(-1) and 0.31 m s(-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 m s(-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 m s(-1) (normal double differential), 4.23 m s(-1) (longitudinal double differential), 4.61 m s(-1) (transverse double differential), 4.64 m s(-1) (transverse single differential) and 4.03 m s(-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.

Mechanomyography versus electromyography, in monitoring the muscular fatigue

BACKGROUND

The use of the mechanomyogram (MMG) which detects muscular vibrations generated by fused individual fiber twitches has been refined. The study addresses a comparison of the MMG and surface electromyogram (SEMG) in monitoring muscle fatigue.

METHODS

The SEMG and MMG were recorded simultaneously from the same territory of motor units in two muscles (Biceps, Brachioradialis) of the human (n = 18), during sustained contraction at 25 % MVC (maximal voluntary contraction).

RESULTS

The RMS (root mean square) of the SEMG and MMG increased with advancing fatigue; MF (median frequency) of the PSD (power density spectra) progressively decreased from the onset of the contraction. These findings (both muscles, all subjects), demonstrate both through the SEMG and MMG a central component of the fatigue. The MF regression slopes of MMG were closer to each other between men and women (Biceps 1.55%; Brachialis 13.2%) than were the SEMG MF slopes (Biceps 25.32%; Brachialis 17.72%), which shows a smaller inter-sex variability for the MMG vs. SEMG.

CONCLUSION

The study presents another quantitative comparison (MF, RMS) of MMG and SEMG, showing that MMG signal can be used for indication of the degree of muscle activation and for monitoring the muscle fatigue when the application of SEMG is not feasible (chronical implants, adverse environments contaminated by electrical noise).

Effect of age on muscle functions investigated with surface electromyography

The purpose of this study was to investigate changes in the surface electromyographic (EMG) signal as a means of defining age-related central and peripheral mechanisms affecting muscle fatigue. Spectral and temporal variables of the surface EMG signal were studied during voluntary isometric contractions of the dominant biceps brachii muscle in a group of 8 healthy elderly men (age range 67-86 years) and a group of 10 healthy young men (age range 23-34 years). The maximal torque developed and the rate of decrease (slope) of spectral variables and conduction velocity (CV) were statistically higher in the young subjects than in the elderly subjects. Motor unit (MU) CV distribution was also estimated from the surface EMG signal and no statistical difference was observed in its variance between the two groups. These results confirm previous findings from the tibialis anterior muscle. Thus, changes in fiber type distribution and decrease in MU firing rate with aging may be factors determining the decrease in maximal voluntary contraction torque and in myoelectric manifestations of muscle fatigue.

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays

This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 m s(-1); the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 m s(-1) and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.

Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions

Many algorithms have been described in the literature for estimating amplitude, frequency variables and conduction velocity of the surface EMG signal detected during voluntary contractions. They have been used in different application areas for the non invasive assessment of muscle functions. Although many studies have focused on the comparison of different methods for information extraction from surface EMG signals, they have been carried out under different conditions and a complete comparison is not available. It is the purpose of this paper to briefly review the most frequently used algorithms for EMG variable estimation, compare them using computer generated as well as real signals and outline the advantages and drawbacks of each. In particular the paper focuses on the issue of EMG amplitude estimation with and without pre-whitening of the signal, mean and median frequency estimation with periodogram and autoregressive based algorithms both in stationary and non-stationary conditions, delay estimation for the calculation of muscle fiber conduction velocity.

Modeling of surface myoelectric signals--Part II: Model-based signal interpretation

Experimental electromyogram (EMG) data from the human biceps brachii were simulated using the model described in [10] of this work. A multichannel linear electrode array, spanning the length of the biceps, was used to detect monopolar and bipolar signals, from which double differential signals were computed, during either voluntary or electrically elicited isometric contractions. For relatively low-level voluntary contractions (10%-30% of maximum force) individual firings of three to four-different motor units were identified and their waveforms were closely approximated by the model. Motor unit parameters such as depth, size, fiber orientation and length, location of innervation and tendonous zones, propagation velocity, and source width were estimated using the model. Two applications of the model are described. The first analyzes the effects of electrode rotation with respect to the muscle fiber direction and shows the possibility of conduction velocity (CV) over- and under-estimation. The second focuses on the myoelectric manifestations of fatigue during a sustained electrically elicited contraction and the interrelationship between muscle fiber CV, spectral and amplitude variables, and the length of the depolarization zone. It is concluded that a) surface EMG detection using an electrode array, when combined with a model of signal propagation, provides a useful method for understanding the physiological and anatomical determinants of EMG waveform characteristics and b) the model provides a way for the interpretation of fatigue plots.

Detection of hidden rhythms in surface EMG signals with a non-linear time-series tool

The analysis of the surface electromyographic (sEMG) signal is particularly attractive because it provides relatively easy access to those physiological processes that allow the muscle to generate force and movement. In this paper, one of the possible applications of recurrence plot strategy to the analysis of sEMG is described. Recurrence Quantification Analysis (RQA) is an efficient time-series analysis tool pertaining to the class of non-linear dynamics time-domain processing. We analysed sEMG recorded on the biceps brachii during isometric contraction both at constant (CF) and non constant force (NCF). For comparison purposes, experimental data were analysed over epochs of 1 s so that the hypothesis of sEMG stationarity could be accepted. The analysis concerned one of the most widely used frequency parameters (namely the median frequency, MDF) and one parameter (i.e., the percent determinism %DET) extracted using the non-linear technique. Our main results are: (i) the gross average evaluated for all subjects on %DET data shows a comparable variation with respect to MDF throughout the course of CF experiments; (ii) %DET seems able to detect motor unit (MU) synchronisation; (iii) during non constant force experiments, %DET is more effective than MDF in detecting sEMG changes determined by brisk transients of force output.

Repeatability of surface EMG variables during voluntary isometric contractions of the biceps brachii muscle

The repeatability of initial value and rate of change of mean spectral frequency (MNF), average rectified values (ARV) and muscle fiber conduction velocity (CV) was investigated in the dominant biceps brachii of ten normal subjects during sustained isometric voluntary contractions. Four levels of contraction were studied: 10%, 30%, 50% and 70% of the maximal voluntary contraction level (MVC). Each contraction was repeated three times in each of three different days for a total of nine contractions/level/subject and 90 contractions per level across the ten subjects. Repeatability was investigated using the Intraclass Correlation Coefficient (ICC) and the standard error of the mean (SEM) of the estimates for each subject. Contrary to observations in other muscles, CV estimates appeared to be very repeatable both within and between subjects. CV showed a small but significant increase when contraction force increased from 10% to 50% MVC but no change for further increase of force. As force increased, MNF showed a slight decrease possibly related to a wider spreading of the CV values. The rate of time decrement of MNF and CV increased with the level of contraction. The normalized decrement (% of initial value per second) was in general higher for MNF than for CV and was more repeatable between subjects at 10% MVC than at 70% MVC. A final observation is that a resting time of 5 minutes may not be sufficient after a contraction at 50% or 70% MVC.

Dynamic changes in electrogram morphology at functional lines of block in reentrant circuits during ventricular tachycardia in the infarcted canine heart: a new method to localize reentrant circuits from electrogram features using adaptive template matching

INTRODUCTION

Fractionated, low-amplitude or long-duration electrograms have limited specificity for locating reentrant circuits causing ventricular tachycardia (VT). In this study a new method is described, adaptive template matching (ATM), based on the quantification of beat-to-beat changes in electrograms, for locating functional reentrant circuits that are relatively stable and cause monomorphic VT.

METHODS AND RESULTS

Monomorphic VTs were induced in 4-day-old infarcted canine hearts by programmed stimulation and reentrant circuits mapped in the epicardial border zone with a 196 or 312 bipolar electrode array. For ATM analysis, a template electrogram from each electrode, during an early cycle, was matched with all subsequent (input) electrograms at the same site by weighting the inputs of amplitude, duration, average baseline, and phase lag. The mean square error (MSE) between template and input was the criterion used to adapt the weights, and was also a measure of changes in electrogram shape that occur from cycle to cycle. The variance of each of the weighting parameters at all electrode sites were plotted on a representation of the electrode array, and the location of the functional lines of block bounding the central common pathway of reentrant circuits with figure-of-eight characteristics, overlaid on the ATM map. Peaks of high variance were found to be coincident with functional lines of block during all tachycardia episodes.

CONCLUSION

Specific beat-to-beat changes in electrograms occur at functional lines of block in reentrant circuits that can be quantified by ATM analysis, suggesting that these regions might be located without activation mapping. The method might be useful to guide ablation catheter position.

Surface EMG signal processing during isometric contractions

This paper provides an overview of techniques suitable for the estimation, interpretation and understanding of time variations that affect the surface electromyographic (EMG) signal during sustained voluntary or electrically elicited contractions. These variations concern amplitude variables, spectral variables and muscle fiber conduction velocity, are interdependent and are referred to as the 'fatigue plot'. The fatigue plot provides information suitable for the classification of muscle behavior. In addition, the information obtainable by means of linear electrode arrays is discussed, and applications of mathematical models for the interpretation of array signals are presented. The model approach provides additional ways for the classification of muscle behavior.

Estimation of shape characteristics of surface muscle signal spectra from time domain data

Myoelectric manifestations of muscle fatigue have been described by monitoring the first-order moment (mean frequency) of the power spectral density function during voluntary or electrically elicited sustained contractions. Higher order central moments provide additional information about the width, skewness, and kurtosis of the spectrum and its shape changes, thereby providing a description of slow nonstationarities more accurate than that allowed by the mean frequency alone. In 1986, B. Saltzberg introduced a method of representing the moments of the power spectral density function of band limited signals, without computing the Fourier transform, as weighted sums of samples of the autocorrelation function. If we allow for oversampling of the signal (and therefore of its autocorrelation function), more efficient weighted sums can be found which give Saltzberg's formula as a limiting case. The faster rate of decay of the weights implies a faster convergence of the estimates and the need to compute fewer samples of the autocorrelation function. The algorithm is particularly suitable for: 1) analysis of evoked potentials (M-waves), because it does not need zero padding to increase resolution and operates on any number of samples, and 2) on-line implementation by dedicated microprocessors performing simultaneous spectral moment analysis on a number of parallel channels.