Automatic detection of motor unit innervation zones of the external anal sphincter by multichannel surface EMG

A method to detect automatically the location of innervation zones (IZs) from 16-channel surface EMG (sEMG) recordings from the external anal sphincter (EAS) muscle is presented in order to guide episiotomy during child delivery. The new algorithm (2DCorr) is applied to individual motor unit action potential (MUAP) templates and is based on bidimensional cross correlation between the interpolated image of each MUAP template and two images obtained by flipping upside-down (around a horizontal axis) and left–right (around a vertical axis) the original one. The method was tested on 640 simulated MUAP templates of the sphincter muscle and compared with previously developed algorithms (Radon Transform, RT; Template Match, TM). Experimental signals were detected from the EAS of 150 subjects using an intra-anal probe with 16 equally spaced circumferential electrodes. The results of the three algorithms were compared with the actual IZ location (simulated signal) and with IZ location provided by visual analysis (VA) (experimental signals). For simulated signals, the inter quartile error range (IQR) between the estimated and the actual locations of the IZ was 0.20, 0.23, 0.42, and 2.32 interelectrode distances (IED) for the VA, 2DCorr, RT and TM methods respectively.     

The effects of electrode placement and innervation zone location on the electromyographic amplitude and mean power frequency versus isometric torque relationships for the vastus lateralis muscle.

The purposes of this investigation were to examine the effects of electrode placement and innervation zone (IZ) location on: (a) the torque-related patterns of responses for absolute and normalized electromyographic (EMG) amplitude and mean power frequency (MPF) and (b) the mean absolute and normalized EMG amplitude and MPF values. In addition, the present study examined the variability between subjects for the location of the IZ for the vastus lateralis (VL). Eight men (mean+/-SD age=23.0+/-4.3yr) performed submaximal to maximal isometric muscle actions of the dominant leg extensors. During each muscle action, fifteen channels of bipolar surface EMG signals were detected from the vastus lateralis using a linear electrode array aligned with the long axis of the muscle fibers. The results indicated that there were differences among channels 1-15 for the patterns of responses and mean values for absolute and normalized EMG amplitude and MPF versus isometric torque. Thus, normalized EMG amplitude and MPF values from different individuals cannot be compared if the EMG signals were detected from different locations over the muscle. In addition, absolute and relative (to femur length) estimates of IZ location for the VL resulted in similar inter-subject variability.     

Estimation of monopolar signals from sphincter muscles and removal of common mode interference

The detection of surface electromyogram (EMG) by multi-electrode systems is applied in many research studies. The signal is usually recorded by means of spatial filters (linear combination of the potential under at least two electrodes) with vanishing sum of weights. Nevertheless, more information could be extracted from monopolar signals measured with respect to a reference electrode away from the muscle. Under certain conditions, surface EMG signal along a curve parallel to the fibre path has zero mean (property approximately satisfied when EMG is sampled by an array of electrodes that covers the entire support of the signal in space). This property allows estimating monopolar from single differential (SD) signals by pseudoinversion of the matrix relating monopolar to SD signals. The method applies to EMG signals from the external anal sphincter muscle, recorded using a specific cylindrical probe with an array of electrodes located along the circular path of the fibres. The performance of the algorithm for the estimation of monopolar from SD signals is tested on simulated signals. The estimation error of monopolar signals decreases by increasing the number of channels. Using at least 12 electrodes, the estimation error is negligible. The method applies to single fibre action potentials, single motor unit action potentials, and interference signals.The same method can also be applied to reduce common mode interference from SD signals from muscles with rectilinear fibres. In this case, the last SD channel defined as the difference between the potentials of the last and the first electrodes must be recorded, so that the sum of all the SD signals vanishes. The SD signals estimated from the double differential signals by pseudoinvertion are free of common mode.     

Reliability of surface EMG matrix in locating the innervation zone of upper trapezius muscle

The identification of the motor unit (MU) innervation zone (IZ) using surface electromyographic (sEMG) signals detected on the skin with a linear array or a matrix of electrodes has been recently proposed in the literature. However, an analysis of the reliability of this procedure and, therefore, of the suitability of the sEMG signals for this purpose has not been reported.The purpose of this work is to describe the intra and inter-rater reliability and the suitability of surface EMG in locating the innervation zone of the upper trapezius muscle.Two operators were trained on electrode matrix positioning and sEMG signal analysis. Ten healthy subjects, instructed to perform a series of isometric contractions of the upper trapezius muscle participated in the study. The two operators collected sEMG signals and then independently estimated the IZ location through visual analysis.Results showed an almost perfect agreement for intra-rater and inter-rater reliability. The constancy of IZ location could be affected by the factors reflecting the population of active MUs and their IZs, including: the contraction intensity, the acquisition period analyzed, the contraction repetition. In almost all cases the IZ location shift due to these factors did not exceed 4 mm. Results generalization to other muscles should be made with caution.     

Assessing redistribution of muscle innervation zones after spinal cord injuries

This study aimed to examine the redistribution of neuromuscular junctions or innervation zones (IZs) after spinal cord injuries (SCI). Fifteen able-bodied subjects and 15 subjects with SCI (American Spinal Injury Association Impairment Scale A to D), participated in the study. Surface electromyography (EMG) signals were collected from the biceps brachii muscle by a customized linear electrode array when subjects generated maximal isometric voluntary contractions. The Radon transform was applied to detect the IZ locations in the multiple channel surface EMG signals which were differentiated between consecutive channels. The distribution of IZs was compared between the SCI and control groups using the student-t test. Statistical analysis disclosed a significantly wider range of IZs in the SCI group compared with the control group (SCI: 3.83 ± 1.32 IED, control: 2.83 ± 0. 87 IED, IED: inter-electrode distance, p < 0.05). No remarkable shifts of the center of the distribution were observed between the two groups (SCI: 9.23 ± 2.35 IED, control: 8.53 ± 2.33 IED, p = 0.42). Changes of IZ distribution in the paralyzed muscles could be associated with the complex neuromuscular reorganization after the SCI.     

Reliability of muscle fibre conduction velocity distribution estimation from surface EMG

Most of the neuromuscular diseases induce changes in muscle fibre characteristics. For example, Duchenne dystrophy is characterized by a specific loss of fast fibres, and an increase in small diameter fibres. These morphological changes may lead to large modifications in the distribution of fibre diameters, possibly producing bimodal distributions. It has already been shown that it is possible to reveal these morphological modifications through the distribution of muscle fibre conduction velocity (MFCV) as estimated from needle electromyography (EMG) recordings. In this paper, we investigate whether such changes can be extracted from surface EMG signals.

Simulation allows generation of surface EMG signals in which features are well described especially at a morphological level. Therefore, we generated a database of simulated signals both in voluntary and electrically elicited contraction conditions using a bimodal distribution of muscle fibre diameters. MFCV distributions were computed using two short-term methods based on cross-correlation and peak-to-peak techniques for voluntary contraction signals, and using a deconvolution method in time domain for electrically elicited signals. MFCV distributions were compared with true ones, as generated from modelling.

This work reveals that estimating MFCV distribution through these methods does not appear yet as precise and robust enough to accurately characterize changes in redistribution of various muscle fibre diameters. However, it opens to new experimental protocols that can be explored in order to improve the robustness of MFCV distribution estimation for the follow-up of patients suffering from neuromuscular disorders.     

The linear electrode array: a useful tool with many applications.

In this review we describe the basic principles of operation of linear electrode arrays for the detection of surface EMG signals, together with their most relevant current applications. A linear array of electrodes is a system which detects surface EMG signals in a number of points located along a line. A spatial filter is usually placed in each point for signal detection, so that the recording of EMG signals with linear arrays corresponds to the sampling in one spatial direction of a spatially filtered version of the potential distribution over the skin. Linear arrays provide indications on motor unit (MU) anatomical properties, such as the locations of the innervation zones and tendons, and the fiber length. Such systems allow the investigation of the properties of the volume conductor and its effect on surface detected signals. Moreover, linear arrays allow to estimate muscle fiber conduction velocity with a very low standard deviation of estimation (of the order of 0.1-0.2 m/s), thus providing reliable indications on muscle fiber membrane properties and their changes in time (for example with fatigue or during treatment). Conduction velocity can be estimated from a signal epoch (global estimate) or at the single MU level. In the latter case, MU action potentials are identified from the interference EMG signals and conduction velocity is estimated for each detected potential. In this way it is possible, in certain conditions, to investigate single MU control and conduction properties with a completely non-invasive approach. Linear arrays provide valuable information on the neuromuscular system properties and appear to be promising tools for applied studies and clinical research.     

Distribution of innervation zone and muscle fiber conduction velocity in the biceps brachii muscle

The spatial distributions of muscle innervation zone (IZ) and muscle fiber conduction velocity (CV) were examined in nine healthy young male participants. High-density surface electromyography (EMG) was collected from the biceps brachii muscle when subjects performed isometric elbow flexions at 20% to 80% of the maximal voluntary contraction (MVC). A total of 9498 samples of IZs were identified and CVs were calculated using the Radon transform. The center and width of IZ sample distribution were compared within four different force levels and six medial to lateral electrode column positions using repeated measures ANOVA and multiple comparison tests. Significant shifts of IZ center were observed in the medial columns (Columns 5, 6, and 7) compared with the lateral columns (Columns 3 and 4) (p < 0.05). Similarly, significant differences in the IZ width were found in Column 7 and 8 compared to Column 3 (p < 0.05). In contrast, muscle CV was unaffected by column position. Instead, muscle CV was faster at 40% and 80% MVC compared to 20% MVC (p < 0.05). The findings of this study add further insights into the physiological properties of the biceps brachii muscle.     

Location of innervation zone determined with multichannel surface electromyography using an optical flow technique.

Multichannel surface electromyography has developed towards more channels and higher spatial resolution. This allows the study of multichannel electromyograms as images of the potential distribution on the skin. In this paper, a method that estimates the motion of the potential distribution using an optical-flow-based technique is introduced. The optical flow is a vector field that describes how images change with time. The aim of this study was to introduce a new method for innervation zone (IZ) localization and to evaluate its performance. The new method was compared with a method that uses the position of the lowest root-mean-square (RMS) value in an electrode array as an estimate of the IZ localization. Comparisons were made with both simulated signals and with recorded multichannel electromyogram signals. Simulations showed that the methods performed similarly for high signal-to-noise ratio (SNR) and that the optical-flow-based method was superior for lower SNR. When the experimental signals were used, localization with the optical-flow-based method gave a mean absolute deviation of 2.4mm from the location given by an expert group. The lowest RMS method gave a significantly higher deviation (13.6mm). Due to the low computational complexity of the optical flow algorithm it is possible to get the estimations of the IZ localization in real time.     

Further evaluation of an EMG technique for assessment of the deep cervical flexor muscles

A novel surface electromyographic (EMG) technique was recently described for the detection of deep cervical flexor muscle activity. Further investigation of this technique is warranted to ensure EMG activity from neighbouring muscles is not markedly influencing the signals recorded. This study compared deep cervical flexor (DCF) muscle activity with the activity of surrounding neck and jaw muscles during various anatomical movements of the neck and jaw in 10 volunteer subjects. DCF EMG activity was recorded with custom electrodes inserted via the nose and fixed by suction to the posterior mucosa of the oropharynx. Surface electrodes were placed over the sternocleidomastoid, anterior scalene, masseter and suprahyoid muscles. Positioned in supine, subjects performed isometric cranio-cervical flexion, cervical flexion, right and left cervical rotation, jaw clench and resisted jaw opening. Across all movements examined, EMG amplitude of the DCF muscles was greatest during neck movements that would require activity of the DCF muscles, particularly during cranio-cervical flexion, their primary anatomical action. The actions of jaw clench and resisted jaw opening demonstrated significantly less DCF EMG activity than the cranio-cervical flexion action (p < 0.05). Across all other movements, the neighbouring neck and jaw muscles demonstrated greatest EMG amplitude during their respective primary anatomical actions, which occurred in the absence of increased EMG amplitude recorded from the DCF muscles. The finding of substantial EMG activity of the DCF muscles only during neck actions that would require their activity, particularly cranio-cervical flexion, and not during actions involving the jaw, provide further assurance that the majority of myoelectric signals detected from the nasopharyngeal electrode are from the DCF muscles.     

Can surface electromyography improve surgery planning? Electromyographic assessment and intraoperative verification of the nerve bundle entry point location of the gracilis muscle

Purpose: To verify the precision of surface electromyography (sEMG) in locating the innervation zone of the gracilis muscle, by comparing the location of the IZ estimated by means of sEMG with in vivo location of the nerve bundle entry point in patients before graciloplasty procedure due to fecal incontinence. Methods: Nine patients who qualified for the graciloplasty procedure underwent sEMG on both gracilis muscle before their operations. During surgery the nerve bundle was identified by means of electrical stimulation. The distance between the proximal attachment and the nerve entry point into the muscle’s body was measured. Both measurements (sEMG and in vivo identification) were compared for each subject. Results: On average, the IZ was located 65.5 mm from the proximal attachment. The mean difference in location of the innervation zones in each individual was 10 ± 9.7 mm, maximal – 30 mm, the difference being statistically significant (p = 0.017). It was intraoperatively confirmed, that the nerve entered the muscle an average of 62 mm from the proximal attachment. The largest difference between the EMG IZ estimation and nerve bundle entry point was 5 mm (mean difference 2.8 mm, p = 0.767). Conclusion: Preoperative surface electromyography of both gracilis muscles is a safe, precise and reliable method of assessing the location of the innervation zones of the gracilis muscles. The asymmetry of the IZ location in left and right muscles may be important in context of technical aspects of the graciloplasty procedure.     

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported.     

Separation of propagating and non propagating components in surface EMG

Surface electromyogram (EMG) detected by electrode arrays along the muscle fibre direction can be approximated by the sum of propagating and non propagating components. A technique to separate propagating and non propagating components in surface EMG signals is developed. The first step is an adaptive filter, which allows obtaining an estimation of the delay between signals detected at different channels and a first estimate of propagating and non propagating components; the second step is used to optimise the estimation of the two components. The method is applicable to signals with one propagating and one non propagating component. It was optimised on simulated signals, and then applied to single motor unit action potentials (MUAP) and to electrically elicited EMG (M-waves).

The new method was first tested on phenomenological signals constituted by the sum of a propagating and a non propagating signal and then applied to simulated and experimental EMG signals. Simulated signals were generated by a cylindrical, layered volume conductor model. Experimental signals were monopolar surface EMG signals collected from the abductor pollicis brevis muscle and M-waves recorded during transcutaneous electrical stimulation of the biceps muscle. The technique may find different applications: in single motor unit (MU) studies (a) for decreasing the variability and bias of CV estimates due to the presence of the non propagating components, (b) for estimating automatically the length of the muscle fibres from only three detected channels and (c) for removal of the stimulation artifact M-waves.     

Neuronal variability during handwriting: lognormal distribution

We examined time-dependent statistical properties of electromyographic (EMG) signals recorded from intrinsic hand muscles during handwriting. Our analysis showed that trial-to-trial neuronal variability of EMG signals is well described by the lognormal distribution clearly distinguished from the Gaussian (normal) distribution. This finding indicates that EMG formation cannot be described by a conventional model where the signal is normally distributed because it is composed by summation of many random sources. We found that the variability of temporal parameters of handwriting--handwriting duration and response time--is also well described by a lognormal distribution. Although, the exact mechanism of lognormal statistics remains an open question, the results obtained should significantly impact experimental research, theoretical modeling and bioengineering applications of motor networks. In particular, our results suggest that accounting for lognormal distribution of EMGs can improve biomimetic systems that strive to reproduce EMG signals in artificial actuators.     

Electrode placement over the innervation zone affects the low-, not the high-frequency portion of the EMG frequency spectrum

The purpose of this study was to use a wavelet-based signal processing technique to examine the influence of electrode placement over the innervation zone (IZ) on the shape of the electromyographic (EMG) frequency spectrum. Ten healthy males (mean ± SD age = 23.6 ± 3.0 years) performed isometric muscle actions of the dominant leg extensors at 10%, 40%, 70%, and 100% of the maximum voluntary contraction (MVC). Surface EMG signals were detected simultaneously from the vastus lateralis with two bipolar electrode arrangements. One of the electrode arrangements had its center point located directly over the IZ, while the other arrangement had its center point distal to the IZ (i.e., 20 mm away). All EMG signals were processed with a wavelet-based procedure. The results showed that for all isometric torque levels, the EMG signals from the distal electrode arrangement demonstrated greater total intensity values than those for the IZ arrangement for frequencies ranging from approximately 2 to 110 Hz. There were no consistent differences, however, between the IZ and distal electrode arrangements for total EMG intensity values above 110 Hz. Thus, these findings indicated that electrode placement over the IZ affected primarily the low-, rather than the high-frequency portion of the EMG frequency spectrum.     

An EMG-CT method using multiple surface electrodes in the forearm

Electromyography computed tomography (EMG-CT) method is proposed for visualizing the individual muscle activities in the human forearm. An EMG conduction model was formulated for reverse-estimation of muscle activities using EMG signals obtained with multi surface electrodes. The optimization process was calculated using sequential quadratic programming by comparing the estimated EMG values from the model with the measured values. The individual muscle activities in the deep region were estimated and used to produce an EMG tomographic image. For validation of the method, isometric contractions of finger muscles were examined for three subjects, applying a flexion load (4.9, 7.4 and 9.8 N) to the proximal interphalangeal joint of the middle finger. EMG signals in the forearm were recorded during the tasks using multiple surface electrodes, which were bound around the subject’s forearm. The EMG-CT method illustrates the distribution of muscle activities within the forearm. The change in amplitude and area of activated muscles can be observed. The normalized muscle activities of all three subjects appear to increase monotonically with increases in the load. Kinesiologically, this method was able to estimate individual muscle activation values and could provide a novel tool for studying hand function and development of an examination for evaluating rehabilitation.     

Variations in the Spatial Distribution of the Amplitude of Surface Electromyograms Are Unlikely Explained by Changes in the Length of Medial Gastrocnemius Fibres with Knee Joint Angle

This study investigates whether knee position affects the amplitude distribution of surface electromyogram (EMG) in the medial gastrocnemius (MG) muscle. Of further concern is understanding whether knee-induced changes in EMG amplitude distribution are associated with regional changes in MG fibre length. Fifteen surface EMGs were acquired proximo-distally from the MG muscle while 22 (13 male) healthy participants (age range: 23–47 years) exerted isometric plantar flexion at 60% of their maximal effort, with knee fully extended and at 90 degrees flexion. The number of channels providing EMGs with greatest amplitude, their relative proximo-distal position and the EMG amplitude averaged over channels were considered to characterise changes in myoelectric activity with knee position. From ultrasound images, collected at rest, fibre length, pennation angle and fat thickness were computed for MG proximo-distal regions. Surface EMGs detected with knee flexed were on average five times smaller than those collected during knee extended. However, during knee flexed, relatively larger EMGs were detected by a dramatically greater number of channels, centred at the MG more proximal regions. Variation in knee position at rest did not affect the proximo-distal values obtained for MG fibre length, pennation angle and fat thickness. Our main findings revealed that, with knee flexion: i) there is a redistribution of activity within the whole MG muscle; ii) EMGs detected locally unlikely suffice to characterise the changes in the neural drive to MG during isometric contractions at knee fully extended and 90 degrees flexed positions; iii) sources other than fibre length may substantially contribute to determining the net, MG activation.     

Innervation zones location and optimal electrodes position of obliquus internus and obliquus externus abdominis muscles

The assessment of abdominal muscles has became popular in recent years because the study of “core muscles” is now considered a pivotal approach for a number of fields. The purpose of this study was to describe the innervation zone (IZ) locations and optimal electrode sites in two core muscles: the obliquus externus (OE) and the obliquus internus (OI) abdominis muscles. Twenty healthy male subjects were recruited and the IZ location was studied during a submaximal isometric contraction using multichannel surface EMG.The optimal electrode position for OI was found to be 2 cm lower the most prominent point of the anterior superior iliac spine, just medial and superior to the inguinal ligament. The optimal electrode position for OE was found to be 14 cm from the median line, lower the level of 1 cm above umbilicus, parallel to the line extending from the most inferior point of the costal margin to the opposite pubic tubercle (almost 45° with respect to the median line).Findings showed that for OI and OE muscles it is possible to provide indications for a muscle belly area suited for proper positioning of at least an electrode pair.     

Electromyographic measures of muscle activation and changes in muscle architecture of human elbow flexors during fatiguing contractions.

The study compared changes in intramuscular and surface recordings of EMG amplitude with ultrasound measures of muscle architecture of the elbow flexors during a submaximal isometric contraction. Ten subjects performed a fatiguing contraction to task failure at 20% of maximal voluntary contraction force. EMG activity was recorded in biceps brachii, brachialis, and brachioradialis muscles using intramuscular and surface electrodes. The rates of increase in the amplitude of the surface EMG for the long and short heads of biceps brachii and brachioradialis were greater than those for the intramuscular recordings measured at different depths. The amplitude of the intramuscular recordings from three muscles increased at a similar rate (P = 0.13), as did the amplitude of the three surface recordings from two muscles (P = 0.83). The increases in brachialis thickness (27.7 +/- 5.7 to 30.9 +/- 3.5 mm; P < 0.05) and pennation angle (10.9 +/- 3.5 to 16.5 +/- 4.8 degrees ; P = 0.003) were not associated with the increase in intramuscular EMG amplitude (P > 0.58). The increase in brachioradialis thickness (22.8 +/- 4.8 to 25.5 +/- 3.4 mm; P = 0.0075) was associated with the increase in the amplitude for one of two intramuscular EMG signals (P = 0.007, r = 0.79). The time to failure was more strongly associated with the rate of increase in the amplitude of the surface EMG than that for the intramuscular EMG, which suggests that the surface measurement provides a more appropriate measure of the change in muscle activation during a fatiguing contraction.     

Robust Muscle Activity Onset Detection Using an Unsupervised Electromyogram Learning Framework

Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscle activity onsets. The distribution of the logarithmic power of EMG signal was characterized by a two-component GMM in each frequency band, in which the two components respectively correspond to the posterior distribution of EMG burst and non-burst logarithmic powers. The parameter set of the GMM was sequentially estimated based on maximum likelihood, subject to constraints derived from the relationship between EMG burst and non-burst distributions. An optimal threshold for EMG burst/non-burst classification was determined using the GMM at each frequency band, and the final decision was obtained by a voting procedure. The proposed novel framework was applied to simulated and experimental surface EMG signals for muscle activity onset detection. Compared with conventional approaches, it demonstrated robust performance for low and changing signal to noise ratios in a dynamic environment. The framework is applicable for real-time implementation, and does not require the assumption of non EMG burst in the initial stage. Such features facilitate its practical application.