Catherine Disselhorst-Klug

Development of recommendations for SEMG sensors and sensor placement procedures

The knowledge of surface electromyography (SEMG) and the number of applications have increased considerably during the past ten years. However, most methodological developments have taken place locally, resulting in different methodologies among the different groups of users.A specific objective of the European concerted action SENIAM (surface EMG for a non-invasive assessment of muscles) was, besides creating more collaboration among the various European groups, to develop recommendations on sensors, sensor placement, signal processing and modeling. This paper will present the process and the results of the development of the recommendations for the SEMG sensors and sensor placement procedures. Execution of the SENIAM sensor tasks, in the period 1996-1999, has been handled in a number of partly parallel and partly sequential activities. A literature scan was carried out on the use of sensors and sensor placement procedures in European laboratories. In total, 144 peer-reviewed papers were scanned on the applied SEMG sensor properties and sensor placement procedures. This showed a large variability of methodology as well as a rather insufficient description. A special workshop provided an overview on the scientific and clinical knowledge of the effects of sensor properties and sensor placement procedures on the SEMG characteristics. Based on the inventory, the results of the topical workshop and generally accepted state-of-the-art knowledge, a first proposal for sensors and sensor placement procedures was defined. Besides containing a general procedure and recommendations for sensor placement, this was worked out in detail for 27 different muscles. This proposal was evaluated in several European laboratories with respect to technical and practical aspects and also sent to all members of the SENIAM club (>100 members) together with a questionnaire to obtain their comments. Based on this evaluation the final recommendations of SENIAM were made and published (SENIAM 8: European recommendations for surface electromyography, 1999), both as a booklet and as a CD-ROM. In this way a common body of knowledge has been created on SEMG sensors and sensor placement properties as well as practical guidelines for the proper use of SEMG.

A marker-based measurement procedure for unconstrained wrist and elbow motions

A protocol is proposed to obtain the joint angles of wrist and elbow from tracked triads of surface markers on each limb segment. Cuffs placed on the limb support the rigidity of the triads. Additional markers are used to mark the approximate positions of joints. Corrections of surface marker data for skin motion are derived from a priori knowledge about plausible joint motions. In addition, ill-conditioned states are trapped when the elbow is nearly fully extended. The protocol is applied to sample motions which demonstrate the use and the effect of the corrections. The results show that the model assumptions are reasonable and that accurate joint rotations can be obtained. The correction steps prove to be an essential part of upper-extremity movement analysis.

Surface electromyography and muscle force: Limits in sEMG-force relationship and new approaches for applications

The estimation of the force generated by an activated muscle is of high relevance not only in biomechanical studies but also more and more in clinical applications in which the information about the muscle forces supports the physicians decisions on diagnosis and treatment. The surface electromyographic signal (sEMG) reflects the degree of activation of skeletal muscles and certain that the sEMG is highly correlated to the muscle force. However, the largest disadvantage in predicting the muscle force from sEMG is the fact that the force generated by a muscle cannot be directly measured non-invasively. Indirect measurement of muscle force goes along with other unpredictable factors which influence the detected force but not necessarily the sEMG data. In addition, the sEMG is often difficult to interpret correctly. The sEMG-force relationship has been investigated for a long time and numerous papers are available. This review shows the limitations in predicting the muscle force from sEMG signals and gives some perspectives on how these limitations could be overcome, especially in clinical applications, by using novel ways of interpretation.

Movement biomechanics goes upwards: from the leg to the arm

The analysis of lower limb movements has been well established in biomechanics research and clinical applications for a long time. For these studies, powerful and very advanced tools have been developed to measure movement parameters and reaction forces. The main focus of interest aims towards gait movements while the understanding of the basic concepts is supported by numerous models. Definitions of physiological ranges and detection of pathological changes in movements open an increasingly valuable clinical field of application. If, however, the primary function of the upper extremities as highly variable and adaptive organ for manipulating tasks is the subject of interest, the situation becomes considerably more complex. The nature of free arm movements is completely different from being restricted, repeatable or cyclic as compared to gait. Therefore, the transfer of the knowledge and experience gained in lower extremity movement analysis to the analysis of upper extremities turns out to be difficult. A proposal for how to proceed in measurements, e.g. where to place the markers and how to calculate movements and angles of segments involved, will be discussed which results in the description of the joint movements of wrist, elbow and shoulder joint. The definition of the motion is a specific step in upper extremity motion analysis which is important in terms of repeatability and significance of the results. An example of assessing movement disorders in children with plexus lesion will illustrate the implications and the potential of upper extremity movement analysis in clinical applications.

Improvement of spatial resolution in surface-EMG: a theoretical and experimental comparison of different spatial filters

In the present study, different isotropic and anisotropic filters have been compared by means of theoretical field simulations and experiments in volunteers. A tripole model for an excited motor unit (MU) was used as the basis for simulating the spatial extension of the filter response for each of the investigated filters. The spatial extension is an indicative of the spatial resolution. For the experimental validation, the total number of single motor units was not directly investigated, but the signal-to-noise ratio (SNR) has been determined. Therefore, the potential distribution generated on the skin surface during maximum voluntary contraction has been simultaneous spatially filtered with each of the investigated filters. The simulations show that an isotropic spatial filtering procedure reduces the spatial extension of the filter response and improves the spatial resolution of the electromyography (EMG)-recording arrangement in comparison to anisotropic spatial filters up to 30%. In other words, the spatial selectivity of the arrangement is increased. This improvement in the filter performance is more pronounced for MUs located close to the skin surface than for MUs more distantly located. Additionally, this theoretical improvement in selectivity depends on the direction of the excitation spread relative to the filter alignment. However, the investigations also show that isotropic filters offer an advantage, compared to anisotropic filters, only when the investigated MU is located extremely close to the filter input. The results of the simulations can be confirmed by the experimental investigations. An improvement of 11% in the SNR, relative to anisotropic spatial filters, can be established when using an isotropic spatial filter. This experimental improvement in selectivity is less than the theoretical improvement because the experimentally investigated MUs have less portion in the anisotropic range of the filters than the simulated one at best.

Principles of High-Spatial-Resolution Surface EMG (HSR-EMG): Single Motor Unit Detection and Application in the Diagnosis of Neuromuscular Disorders

The most detailed information about the structural and functional characteristics of the muscle can be gained from the single motor unit (MU) action potential. In addition, information about the activity of a single MU is essential for the diagnosis of neuromuscular disorders. Due to the low spatial resolution of conventional bipolar surface electromyography (EMG), the resulting signal is a superposition of a large number of simultaneous active MUs. The difficulty is in separating the activity of a single MU from simultaneous active adjacent MUs. In contrast to other non-invasive EMG procedures, the high-spatial-resolution-EMG (HSR-EMG), which is based on the use of a multi-electrode array in combination with a spatial filter procedure, allows the detection of single MU activity in a non-invasive way. It opens access to the excitation spread and enables the determination of the conduction velocity in single MUs, and the localization of the endplate region. In addition, HSR-EMG detects changes in the electrical activities of the MUs which are typical in neuromuscular disorders. Using HSR-EMG it was possible to identify 97% of all investigated volunteers and patients with muscular or neuronal disorders. Therefore, HSR-EMG is suitable as a tool for the non-invasive diagnosis of neuromuscular disorders.

Diagnostic yield of noninvasive high spatial resolution electromyography in neuromuscular diseases

High Spatial Resolution electromyography (HSR‐EMG), a new kind of noninvasive surface EMG based on a spatial filtering technique, was evaluated with respect to the diagnosis of neuromuscular diseases. HSR‐EMG measurements were recorded from 61 healthy subjects and 72 patients with different neuromuscular diseases and analyzed quantitatively. The results indicate that a few parameters such as muscular conduction velocity, dwell time over root mean square, autocorrelation function, and chi‐value are sufficient to recognize and classify specific signal alterations due to neuromuscular disorders. A diagnostic evaluation procedure calculating automatically the most probable diagnosis from the parameter results could assign the correct diagnosis to about 81% of the investigated patients and healthy subjects. Myopathic disorders were recognized with a sensitivity of 85% (specificity: 97%), neuropathic disorders with a sensitivity of 68% (specificity: 98%). We conclude that HSR‐EMG shows a diagnostic validity similar to that described in literature for needle EMG. Moreover, the noninvasive technique provides the advantage of a simple and painless application.

Simulation analysis of the ability of different types of multi- electrodes to increase selectivity of detection and to reduce cross- talk

Selectivity of different one- and two-dimensional multi-electrodes and their ability to reduce cross-talk were analyzed. Signals from an individual motor unit (MU) were calculated as a single convolution of intracellular action potential (IAP) first temporal derivative and spatially filtered MU impulse response. It was shown that the uptake area (irrespective of the way it was defined) could not characterize electrode properties reliably because its estimate depended on the source parameters. Due to the different decline of individual phases of MU signals with depth, electrode should provide higher spatial and temporal resolution of the main phases for better selectivity and greater suppression of the terminal phases for cross-talk reduction. A two-dimensional normal double differentiating (NDD) electrode provided almost the same or slightly lower selectivity but weaker reduction of cross-talk than a longitudinal double differentiating (LDD) electrode. A transversal double differentiating (TDD) electrode provided a lower selectivity and weaker reduction of cross-talk than a LDD electrode. A new, BiTDD multi-electrode (performing difference between signals detected by two TDD electrodes) provided the best selectivity and reduction of cross-talk. To obtain the smallest cross-talk, a BiTDD electrode should be positioned above the end-plate region, while LDD, TDD, or NDD electrodes-above the ends of muscle that produced it. Signal differentiation improved selectivity but increased cross-talk.

Noninvasive approach to motor unit characterization: Muscle structure, membrane dynamics and neuronal control

The standard surface EMG reflects the compound activity of a high number of motor units which is finally due to its low spatial resolution in the detection of the potential distribution on the skin surface. Therefore, detailed information about the structural and functional characteristics of the muscle consisting of populations of motor units, like the functional anatomy, the excitation spread or the innervation pattern cannot be obtained from the standard surface EMG. A novel noninvasive EMG-procedure with high spatial resolution (HSR-EMG) allows in contrast to the standard surface EMG even the detection of the single motor unit activity. In this way, the noninvasive determination of detailed information about the muscle structure, the membrane dynamics and the neuronal control becomes possible. First applications of the HSR-EMG have shown that especially the noninvasively measured conduction velocity of the excitation is highly affected by physiological details, like the muscle temperature, the relative muscle fibre diameter or inhomogeneities in the connective tissue forming part of the volume conductor around the muscle. From the results of the HSR-EMG investigations it can be concluded that the information about the structural and functional characteristics of the muscle as well as a deeper insight in the active state of the muscle is essential for a correct interpretation of the standard surface EMG.

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