Zoia C. Lateva

EMGLAB: An interactive EMG decomposition program

This paper describes an interactive computer program for decomposing EMG signals into their component motor-unit potential (MUP) trains and for averaging MUP waveforms. The program is able to handle single- or multi-channel signals recorded by needle or fine-wire electrodes during low and moderate levels of muscular contraction. It includes advanced algorithms for template matching, resolving superimpositions, and waveform averaging, as well as a convenient user interface for manually editing and verifying the results. The program also provides the ability to inspect the discharges of individual motor units more closely by subtracting out interfering activity from other MUP trains. Decomposition accuracy was assessed by cross-checking pairs of signals recorded by nearby electrodes during the same contraction. The results show that 100% accuracy can be achieved for MUPs with peak-to-peak amplitudes greater than 2.5 times the rms signal amplitude. Examples are presented to show how decomposition can be used to investigate motor-unit recruitment and discharge behavior, to study motor-unit architecture, and to detect action potential blocking in doubly innervated muscle fibers.

Anatomical and electrophysiological determinants of the human thenar compound muscle action potential

Clinical interpretation of the compound muscle action potential (CMAP) requires a precise understanding of its underlying mechanisms. We recorded normal thenar CMAPs and motor unit action potentials using different electrode configurations and different thumb positions. Computer simulations show that the CMAP has four parts: rising edge, negative phase, positive phase, and tail, which correspond to four distinct stages of electrical activity in the muscle: initiation at the end‐plate, propagation, termination at the muscle/tendon junctions, and slow repolarization. The shapes of volume‐conducted signals recorded beyond the muscle are also explained by these four stages. Changes in CMAP shape associated with thumb abduction are due to changes in termination times resulting from changes in muscle‐fiber lengths. These findings demonstrate that the negative and positive phases of the CMAP are due to different mechanisms, and that anatomical factors, particularly muscle‐fiber lengths, play an important role in determining CMAP shape.

Electrophysiological evidence of adult human skeletal muscle fibres with multiple endplates and polyneuronal innervation

Electromyographic (EMG) signals were recorded using intramuscular electrodes at six different sites in the brachioradialis muscles during voluntary isometric contractions in four subjects. The potential waveforms and discharge patterns of up to 12 simultaneously active motor units were identified from each signal using computer‐aided decomposition. Out of a total of 301 motor unit potentials identified, 23 potentials exhibited behaviour consistent with having been generated by muscle fibres that were innervated by two different motoneurons at widely separated endplates. These potentials discharged in association with two different motor units, but were blocked or delayed whenever the two motor units discharged within a few milliseconds of one another. The blocking was consistent with a collision or refractoriness when one motoneuron tried to excite the fibre while it was already conducting an action potential initiated by the other motoneuron. The delays were consistent with decreased conduction velocity associated with incomplete recovery of the fibre after a preceding action potential. From the temporal separation between the discharges of the two motoneurons that resulted in blocking, the spatial separation between the endplates was estimated to be between 26 and 44 mm. These findings challenge the classical concept of the motor unit as an anatomically distinct and functionally independent entity. It is suggested that the human brachioradialis muscle may contain both long, polyneuronally innervated fibres and short, serially linked, singly innervated fibres.

Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscle

The aim of this study was to assess the accuracy of the convolution kernel compensation (CKC) method in decomposing high-density surface EMG (HDsEMG) signals from the pennate biceps femoris long-head muscle. Although the CKC method has already been thoroughly assessed in parallel-fibered muscles, there are several factors that could hinder its performance in pennate muscles. Namely, HDsEMG signals from pennate and parallel-fibered muscles differ considerably in terms of the number of detectable motor units (MUs) and the spatial distribution of the motor-unit action potentials (MUAPs). In this study, monopolar surface EMG signals were recorded from five normal subjects during low-force voluntary isometric contractions using a 92-channel electrode grid with 8 mm inter-electrode distances. Intramuscular EMG (iEMG) signals were recorded concurrently using monopolar needles. The HDsEMG and iEMG signals were independently decomposed into MUAP trains, and the iEMG results were verified using a rigorous a posteriori statistical analysis. HDsEMG decomposition identified from 2 to 30 MUAP trains per contraction. 3 ± 2 of these trains were also reliably detected by iEMG decomposition. The measured CKC decomposition accuracy of these common trains over a selected 10 s interval was 91.5 ± 5.8%. The other trains were not assessed. The significant factors that affected CKC decomposition accuracy were the number of HDsEMG channels that were free of technical artifact and the distinguishability of the MUAPs in the HDsEMG signal (P < 0.05). These results show that the CKC method reliably identifies at least a subset of MUAP trains in HDsEMG signals from low force contractions in pennate muscles.

Estimating motor-unit architectural properties by analyzing motor-unit action potential morphology.

OBJECTIVE We investigated the architectural organization of groups of neighboring motor units (MUs) in normal brachial biceps and tibialis anterior muscles by analyzing morphological landmarks of their MU action potentials (MUAPs). METHODS EMG signals containing multiple MUAPs were recorded using a monopolar needle electrode during moderate isometric voluntary contractions. The MUAPs were identified using computer-aided decomposition, and averaged. For each MUAP the onset, spike, terminal wave, and slow afterwave were identified; then the location of the MUs endplate and muscle/tendon junction were estimated from the latencies of the spike and terminal wave with respect to the MUAP onset. RESULTS The analysis revealed a variety of architectural organizations, including single and multiple endplate zones, MU fractions, pennation, intramuscular aponeuroses, and centrally and non-centrally located endplates. CONCLUSIONS This type of morphological analysis of the MUAP promises to be informative for studying normal MU properties as well as evaluating MU reorganization in disease.

The physiological origin of the slow afterwave in muscle action potentials

OBJECTIVE Both intramuscularly-recorded motor unit action potentials (MUAPs) and surface recorded MUAPs and compound muscle action potentials (CMAPs) have slow afterwaves which can contribute as much as half their measured duration. This study tested the hypothesis that the slow afterwave has its physiological origin in the negative afterpotential of the muscle fiber intracellular action potential (IAP). METHODS We investigated the slow afterwave in MUAPs and CMAPs from brachial biceps, tibialis anterior, first dorsal interosseous, thenar and hypothenar muscles in 15 normal subjects, and using computer simulations. RESULTS The slow afterwaves did not match the time constant of the amplifiers high-pass filter, and so were not filtering artifacts. They lasted long after propagation had terminated at the muscle/tendon junction, and so were not due to the temporal or spatial dispersion of propagating single-fiber potentials. Their amplitude and polarity varied with the recording site as predicted by computer simulations that modeled the IAP as having a negative afterpotential. They also changed with double-pulse stimulation and decreasing temperature in ways consistent with the results of intracellular studies of the IAP negative afterpotential. CONCLUSIONS The presented results support our hypothesis that the slow afterwave is a manifestation of the IAP negative afterpotential.

A model of the muscle action potential for describing the leading edge, terminal wave, and slow afterwave

The leading edge, terminal wave, and slow afterwave of the motor-unit action potential (MUAP) are produced by changes in the strength of electrical sources in the muscle fibers rather than by movement of sources. The latencies and shapes of these features are, therefore, determined primarily by the motor-unit (MU) architecture and the intracellular action potential (IAP), rather than by the volume-conduction characteristics of the limb. We present a simple model to explain these relationships. The MUAP is modeled as the convolution of a source function related to the IAP and a weighting function related to the MU architecture. The IAP waveform is modeled as the sum of a spike and a slow repolarization phase. The MU architecture is modeled by assuming that the individual fibers lie along a single equivalent axis but that their action potentials have dispersed initiation and termination times. The model is illustrated by simulating experimentally recorded MUAPs and compound muscle action potentials.

Validation of a computer-aided EMG decomposition method

This paper presents an objective assessment of the accuracy of EMGLAB, a computer-aided EMG decomposition program that we developed. EMG signals were recorded simultaneously using monopolar needle and fine-wire electrodes from nearby sites in the tibialis anterior muscle during moderate isometric contractions. The signals were decomposed independently by an experienced operator, yielding 3-12 (mean 8.7) motor-unit action potential (MUAP) trains per signal. Decomposition accuracy was estimated by crosschecking 83 pairs of trains from different signals that corresponded to the same motor units. The results show that EMGLAB was able to decompose large MUAPs (peak amplitudes greater than 2.5 times rms signal amplitude) with 98-100% accuracy, and smaller MUAPs with 80-100% accuracy. Many of the errors involved misalignment of small MUAPs within superimpositions and amounted to less than 5 ms. These results validate the accuracy of EMGLAB for decomposing EMG signals of moderate complexity.

The contribution of the interosseous muscles to the hypothenar compound muscle action potential

The contributions of the various ulnar‐innervated muscles of the hand to the hypothenar compound muscle action potential (CMAP) were estimated by directly stimulating individual muscles and by analyzing CMAP shape changes resulting from manipulations that changed individual muscle lengths. The results show that the first peak of the negative phase of the hypothenar CMAP comes from the hypothenar muscles, but that the second peak is due to a large volume‐conducted potential from the interosseous muscles. The interosseous contribution affects both the amplitude and the area of the CMAP, and makes these parameters sensitive to changes in the configuration of the fingers and the temperature gradient in the hand. To reduce the interosseous contribution, a “balanced reference” consisting of two reference electrodes, one over each tendon, is proposed.

History dependence of human muscle-fiber conduction velocity during voluntary isometric contractions

The conduction velocity (CV) of a muscle fiber is affected by the fibers discharge history going back ∼1 s. We investigated this dependence by measuring CV fluctuations during voluntary isometric contractions of the human brachioradialis muscle. We recorded electromyogram (EMG) signals simultaneously from multiple intramuscular electrodes, identified potentials belonging to the same motor unit using EMG decomposition, and estimated the CV of each discharge from the interpotential interval. In 12 of 14 subjects, CV increased by ∼10% during the first second after recruitment and then fluctuated by about ±2% in a way that mirrored the fluctuations in the instantaneous firing rate. The CV profile could be precisely described in terms of the discharge history by a simple mathematical model. In the other two subjects, and one subject retested after cooling the arm, the CV fluctuations were inversely correlated with instantaneous firing rate. In all subjects, CV was additionally affected by very short interdischarge intervals (<25 ms): it was increased in doublets at recruitment, but decreased in doublets during continuous firing and after short interdischarge intervals in doubly innervated fibers. CV also exhibited a slow trend of about -0.05%/s that did not depend on the immediate discharge history. We suggest that measurements of CV fluctuations during voluntary contractions, or during stimulation protocols that involve longer and more complex stimulation patterns than are currently being used, may provide a sensitive approach for estimating the dynamic characteristics of ion channels in the human muscle-fiber membrane.