Influence of Electromyogram (EMG) Amplitude Processing in EMG-Torque Estimation

A number of studies have investigated the relationship between surface electromyogram (EMG) and torque exerted about a joint. The standard deviation of the recorded EMG signal is defined as the EMG amplitude. The EMG amplitude estimation technique varies with the study from conventional type of processing (i.e. rectification followed by low pass filtering) to further addition of different noise rejection and signal-to-noise ratio improvement stages. Advanced EMG amplitude processors developed recently that incorporate signal whitening and multiple-channel combination have been shown to significantly improve amplitude estimation. The main contribution of this research is a comparison of the performance of EMG-torque estimators with and without these advanced EMG amplitude processors. The experimental data are taken from fifteen subjects that produced constant-posture, non-fatiguing, force-varying contractions about the elbow while torque and biceps/triceps EMG were recorded. Utilizing system identification techniques, EMG amplitude was related to torque through a zeros-only (finite impulse response, FIR) model. The incorporation of whitening and multiple-channel combination separately reduced EMG-torque errors and their combination provided a cumulative improvement. A 15th-order linear FIR model provided an average estimation error of 6% of maximum voluntary contraction (or 90% of variance accounted for) when EMG amplitudes were obtained using a four-channel, whitened processor. The equivalent single-channel, unwhitened (conventional) processor produced an average error of 8% of maximum voluntary contraction (variance accounted for of 68%). This study also describes the occurrence of spurious peaks in estimated torque when the torque model is created from data with a sampling rate well above the bandwidth of the torque. This problem is anticipated when the torque data are sampled at the same rate as the EMG data. The problem is resolved by decimating the EMG amplitude prior to relating it to joint torque, in this case to an effective sampling rate of 40.96 Hz

Automatic analysis of surface electromyography of reflexes for central nervous system inhibition during pregnancy

Eclampsia is a life-threatening neurological complication (seizures or coma) of pregnancy, and represents progression of pre-eclampsia (high blood pressure and protein in the urine isolated to pregnancy). Even though the exact mechanism of pre-eclampsia and its neurological manifestations have yet to be definitively established, it is known that there is a loss of inhibitory impulses between the cerebral cortex and the spinal cord resulting in exaggerated reflexes. In this work, we establish normative measures for deep tendon reflexes (DTR) during pregnancy. We quantified the surface electromyogram (EMG) at the knee and ankle of DTRs in 279 subjects. The signals were analyzed in the time, frequency and time-frequency domains, including normalization to patient characteristics. We uncovered two new phenomena. The first involves gradual prolongation of the reflex signal (termed latency) throughout pregnancy. The second is that the prolongation of latency is equal in the knee and ankle, allowing the conclusion that the prolongation is due to a delay in signal transduction at the synapse in the spinal cord. This delay likely represents central nervous system inhibition, either due to the effect of upper motor neurons in the spine, but more likely due to an inhibitory cortical effect (possibly hormonal). It is postulated that the quantification of the deep tendon reflex will allow patients with pre-eclampsia to be screened more effectively

Surface EMG and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue

In a broad view, fatigue is used to indicate a degree of weariness. On a muscular level, fatigue posits the reduced capacity of muscle fibres to produce force, even in the presence of motor neuron excitation via either spinal mechanisms or electric pulses applied externally. Prior to decreased force, when sustaining physically demanding tasks, alterations in the muscle electrical properties take place. These alterations, termed myoelectric manifestation of fatigue, can be assessed non-invasively with a pair of surface electrodes positioned appropriately on the target muscle; traditional approach. A relatively more recent approach consists of the use of multiple electrodes. This multi-channel approach provides access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; it allows for: (i) a more precise quantification of the propagation velocity, a physiological variable of marked interest to the study of fatigue; (ii) the assessment of regional, myoelectric manifestations of fatigue; (iii) the analysis of single motor units, with the possibility to obtain information about motor unit control and fibre membrane changes. This review provides a methodological account on the multi-channel approach for the study of myoelectric manifestation of fatigue and on the experimental conditions to which it applies, as well as examples of their current applications

Analysis of sEMG on biceps brachii and brachioradialis in static conditions: Effect of joint angle and contraction level

Despite several previous investigations, the direct correlation between the elbow joint angle and the activities of related muscles is still an unresolved topic. The sEMG signals were recorded from biceps brachii (6x8 electrodes, 10mm IED, d=3mm) and brachioradialis (1x8 electrodes, 5mm IED, d=3mm) of ten subjects. The subjects were asked to perform isometric elbow flexion at five joint angles with four contraction levels with respect to the maximum contraction (MVC) at that joint angle. The RMS values of biceps brachii (BB) and brachioradialis (BR) are computed within 500ms epoch and averaged over the muscle’s active region. These values increase along as force increases regardless the joint angle. Concerning the different joint angle, we found that as the arm extended, the RMS values of seven subjects decreased, while the RMS values of three subjects increased. This behavior suggests different strategies of muscle contribution to the task in different subjects but may also be attributed to the technical issues discussed in Chapter 2 - 7. Prior to this investigation, several issues related to the sEMG signals recording and processing were evaluated. Analysis on the effect of different elbow joint angle on the position of the innervations zone (IZ) of biceps brachii muscle indicates that the IZ shifts distally 24±9mm as the subjects extend their arms. Thus to assure sEMG signal recording, a grid of electrodes is selected instead of bipolar electrodes. The issue of spatial aliasing, which has not been addressed before, was studied. Greater electrode’s diameter implies higher spatial low pass filtering effect which gives an advantage as anti-aliasing filter in space. On the other hand, this low pass filtering effect increase the error on the power for the single sEMG image (d=10mm, 10mm IED) to 3±13.5% compared to the continuous image. Larger IED introduces RMS estimation error up to ±18% for the single sEMG image (15mm IED). However, taking the mean of a group of maps, the error of the mean is negligible (<3%). Furthermore, the envelope of the rectified EMG has been investigated. Five digital low pass filters (Butterworth, Chebyshev, Inverse Chebyshev, and Elliptic) with five different orders, four cut off frequencies and one or bi-directional filtering were tested using simulated sEMG interference signals. The results show that different filters are optimal for different applications. Power line interference is one of the sources of impurity of the sEMG signals. Notch filter, spectral interpolation, adaptive filter, and adaptive noise canceller with phase locked loop were compared. Another factor that affects the amplitude of sEMG is the subcutaneous layer thickness (ST). Higher contraction level and greater elbow joint angle lead to thinner ST. RMS values tend to decrease for thicker ST at a rate of 1.62 decade/decade

Introduction to this Special Issue: Intelligent Data Analysis on Electromyography and Electroneurography

Computer-aided electromyography (EMG) and elec- troneurography (ENG) have become indispensable tools in the daily activities of neurophysiology laboratories in facilitating quantitative analysis and decision making in clinical neurophysiology, rehabilitation, sports medicine, and studies of human physiology. These tools form the basis of a new era in the practice of neurophysiology facilitating the: (i) Standardization . Diagnoses obtained with similar criteria in different laboratories can be veri- fied. (ii) Sensitivity . Neurophysiological findings in a particular subject under investigation may be compared with a database of normal values to determine whether abnormality exists or not. (iii) Specificity . Findings may be compared with databases derived from patients with known diseases, to evaluate whether they fit a specific diagnosis. (iv) Equivalence . Results from serial examin- ations on the same patient may be compared to decide whether there is evidence of disease progression or of response to treatment. Also, findings obtained from dif- ferent quantitative methods may be contrasted to deter- mine which are most sensitive and specific. Different methodologies have been developed in com- puter-aided EMG and ENG analysis ranging from simple quantitative measures of the recorded potentials, to more complex knowledge-based and neural network systems that enable the automated assessment of neuromuscular disorders. However, the need still exists for the further advancement and standardization of these method- ologies, especially nowadays with the emerging health telematics technologies which will enable their wider application in the neurophysiological laboratory. The main objective of this Special Issue of Medical Engin- eering & Physics is to provide a snapshot of current activities and methodologies in intelligent data analysis in peripheral neurophysiology. A total of 12 papers are published in this Special Issue under the following topics: Motor Unit Action Potential (MUAP) Analysis, Surface EMG (SEMG) Analysis, Electroneurography, and Decision Systems. In this intro- duction, the papers are briefly introduced, following a brief review of the major achievements in quantitative electromyography and electroneuropathy

A Study of Myoelectric Signal Processing

This dissertation of various aspects of electromyogram (EMG: muscle electrical activity) signal processing is comprised of two projects in which I was the lead investigator and two team projects in which I participated. The first investigator-led project was a study of reconstructing continuous EMG discharge rates from neural impulses. Related methods for calculating neural firing rates in other contexts were adapted and applied to the intramuscular motor unit action potential train firing rate. Statistical results based on simulation and clinical data suggest that performances of spline-based methods are superior to conventional filter-based methods in the absence of decomposition error, but they unacceptably degrade in the presence of even the smallest decomposition errors present in real EMG data, which is typically around 3-5%. Optimal parameters for each method are found, and with normal decomposition error rates, ranks of these methods with their optimal parameters are given. Overall, Hanning filtering and Berger methods exhibit consistent and significant advantages over other methods. In the second investigator-led project, the technique of signal whitening was applied prior to motion classification of upper limb surface EMG signals previously collected from the forearm muscles of intact and amputee subjects. The motions classified consisted of 11 hand and wrist actions pertaining to prosthesis control. Theoretical models and experimental data showed that whitening increased EMG signal bandwidth by 65-75% and the coefficients of variation of temporal features computed from the EMG were reduced. As a result, a consistent classification accuracy improvement of 3-5% was observed for all subjects at small analysis durations (\u3c 100 ms). In the first team-based project, advanced modeling methods of the constant posture EMG-torque relationship about the elbow were studied: whitened and multi-channel EMG signals, training set duration, regularized model parameter estimation and nonlinear models. Combined, these methods reduced error to less than a quarter of standard techniques. In the second team-based project, a study related biceps-triceps surface EMG to elbow torque at seven joint angles during constant-posture contractions. Models accounting for co-contraction estimated that individual flexion muscle torques were much higher than models that did not account for co-contraction

Reliability of muscle fiber conduction velocity in the tibialis anterior

This document could not have been completed without the hard work of a number of individuals. First and foremost, my supervisor, Dr. David Gabriel deserves the utmost recognition for the immense effort and time spent guiding the production of this document through the various stages of completion. Also, aiding in the data collection, technical support, and general thought processing were Lab Technician Greig Inglis and fellow members of the Electromyographic Kinesiology Laboratory Jon Howard, Sean Lenhardt, Lara Robbins, and Corrine Davies-Schinkel. The input of Drs. Ted Clancy, Phil Sullivan and external examiner Dr. Anita Christie, all members ofthe assessment committee, was incredibly important and vital to the completion of this work. Their expertise provided a strong source of knowledge and went to ensure that this project was completed at exemplary level. There were a number of other individuals who were an immense help in getting this project off the ground and completed. The donation of their time and efforts was very generous and much needed in order to fulfill the requirements needed for completion of this study. Finally, I cannot exclude the contributions of my family throughout this project especially that of my parents whose support never wavers