Feature Analysis for Discrimination of Motor Unit Action Potentials

© 2018 IEEE. In electrophysiological signal processing for intramuscular electromyography data (nEMG), single motor unit activity is of great interest. The changes of action potential (MUAP) morphology, motor unit (MU) activation, and recruitment provide the most informative part to study the nature causality in neuromuscular disorders. In practice, for a single nEMG recording, more than one motor unit activities (in the surrounding area of a needle electrode) are usually collected. Such a fact makes the MUAP discrimination that separates single unit activities a crucial task. Most neurology laboratories worldwide still recruit specialists who spend hours to manually or semi-automatically sort MUAPs. From a machine learning perspective, this task is analogous to the clustering-based classification problem in which the number of classes and other class information are unfortunately missing. In this paper, we present a feature analysis strategy to help better utilize unsupervised (i.e., totally automated) methods for MUAP discrimination. To that end, we extract a large pool of features from each MUAP. Then we select the top ranked candidates using clusterability scores as selection criteria. We found spectrograms of wavelet decomposition as a top-ranking feature, highly correlated to the motor unit reference and was more separable than existing features. Using a correlation-based clustering technique, we demonstrate the sorting performance with this feature set. Compared with the reference produced by human experts, our method obtained a comparable result (e.g., equivalent number of classes was found, identical MUAP morphology in each pair of corresponding MU class, and similar histograms of MUs). Taking the manual labels as references, our method got a much higher sensitivity and accuracy than the compared unsupervised sorting method. We obtained a similar result in MUAP classification to the reference

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores

© 2012 IEEE. Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of 96% (79%). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of 94% (84%) for ankle and 89% (94%) for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., 3 s versus 7.5 s) and/or lower tolerance (e.g., 0.4 s versus 2 s)

Effects of the physiological parameters on the signal-to-noise ratio of single myoelectric channel

<p>Abstract</p> <p>Background</p> <p>An important measure of the performance of a myoelectric (ME) control system for powered artificial limbs is the signal-to-noise ratio (SNR) at the output of ME channel. However, few studies illustrated the neuron-muscular interactive effects on the SNR at ME control channel output. In order to obtain a comprehensive understanding on the relationship between the physiology of individual motor unit and the ME control performance, this study investigates the effects of physiological factors on the SNR of single ME channel by an analytical and simulation approach, where the SNR is defined as the ratio of the mean squared value estimation at the channel output and the variance of the estimation.</p> <p>Methods</p> <p>Mathematical models are formulated based on three fundamental elements: a motoneuron firing mechanism, motor unit action potential (MUAP) module, and signal processor. Myoelectric signals of a motor unit are synthesized with different physiological parameters, and the corresponding SNR of single ME channel is numerically calculated. Effects of physiological multi factors on the SNR are investigated, including properties of the motoneuron, MUAP waveform, recruitment order, and firing pattern, etc.</p> <p>Results</p> <p>The results of the mathematical model, supported by simulation, indicate that the SNR of a single ME channel is associated with the voluntary contraction level. We showed that a model-based approach can provide insight into the key factors and bioprocess in ME control. The results of this modelling work can be potentially used in the improvement of ME control performance and for the training of amputees with powered prostheses.</p> <p>Conclusion</p> <p>The SNR of single ME channel is a force, neuronal and muscular property dependent parameter. The theoretical model provides possible guidance to enhance the SNR of ME channel by controlling physiological variables or conscious contraction level.</p

Challenges and New Approaches to Proving the Existence of Muscle Synergies of Neural Origin

Muscle coordination studies repeatedly show low-dimensionality of muscle activations for a wide variety of motor tasks. The basis vectors of this low-dimensional subspace, termed muscle synergies, are hypothesized to reflect neurally-established functional muscle groupings that simplify body control. However, the muscle synergy hypothesis has been notoriously difficult to prove or falsify. We use cadaveric experiments and computational models to perform a crucial thought experiment and develop an alternative explanation of how muscle synergies could be observed without the nervous system having controlled muscles in groups. We first show that the biomechanics of the limb constrains musculotendon length changes to a low-dimensional subspace across all possible movement directions. We then show that a modest assumption—that each muscle is independently instructed to resist length change—leads to the result that electromyographic (EMG) synergies will arise without the need to conclude that they are a product of neural coupling among muscles. Finally, we show that there are dimensionality-reducing constraints in the isometric production of force in a variety of directions, but that these constraints are more easily controlled for, suggesting new experimental directions. These counter-examples to current thinking clearly show how experimenters could adequately control for the constraints described here when designing experiments to test for muscle synergies—but, to the best of our knowledge, this has not yet been done

Conventionally assessed voluntary activation does not represent relative voluntary torque production

The ability to voluntarily activate a muscle is commonly assessed by some variant of the twitch interpolation technique (ITT), which assumes that the stimulated force increment decreases linearly as voluntary force increases. In the present study, subjects (n = 7) with exceptional ability for maximal voluntary activation (VA) of the knee extensors were used to study the relationship between superimposed and voluntary torque. This includes very high contraction intensities (90–100%VA), which are difficult to consistently obtain in regular healthy subjects (VA of ∼90%). Subjects were tested at 30, 60, and 90° knee angles on two experimental days. At each angle, isometric knee extensions were performed with supramaximal superimposed nerve stimulation (triplet: three pulses at 300 Hz). Surface EMG signals were obtained from rectus femoris, vastus lateralis, and medialis muscles. Maximal VA was similar and very high across knee angles: 97 ± 2.3% (mean ± SD). At high contraction intensities, the increase in voluntary torque was far greater than would be expected based on the decrement of superimposed torque. When voluntary torque increased from 79.6 ± 6.1 to 100%MVC, superimposed torque decreased from 8.5 ± 2.6 to 2.8 ± 2.3% of resting triplet. Therefore, an increase in VA of 5.7% (from 91.5 ± 2.6 to 97 ± 2.3%) coincided with a much larger increase in voluntary torque (20.4 ± 6.1%MVC) and EMG (33.9 ± 6.6%max). Moreover, a conventionally assessed VA of 91.5 ± 2.6% represented a voluntary torque of only 79.6 ± 6.1%MVC. In conclusion, when maximal VA is calculated to be ∼90% (as in regular healthy subjects), this probably represents a considerable overestimation of the subjects’ ability to maximally drive their quadriceps muscles

Vibration-induced extra torque during electrically-evoked contractions of the human calf muscles

<p>Abstract</p> <p>Background</p> <p>High-frequency trains of electrical stimulation applied over the lower limb muscles can generate forces higher than would be expected from a peripheral mechanism (i.e. by direct activation of motor axons). This phenomenon is presumably originated within the central nervous system by synaptic input from Ia afferents to motoneurons and is consistent with the development of plateau potentials. The first objective of this work was to investigate if vibration (sinusoidal or random) applied to the Achilles tendon is also able to generate large magnitude extra torques in the triceps surae muscle group. The second objective was to verify if the extra torques that were found were accompanied by increases in motoneuron excitability.</p> <p>Methods</p> <p>Subjects (n = 6) were seated on a chair and the right foot was strapped to a pedal attached to a torque meter. The isometric ankle torque was measured in response to different patterns of coupled electrical (20-Hz, rectangular 1-ms pulses) and mechanical stimuli (either 100-Hz sinusoid or gaussian white noise) applied to the triceps surae muscle group. In an additional investigation, M_maxand F-waves were elicited at different times before or after the vibratory stimulation.</p> <p>Results</p> <p>The vibratory bursts could generate substantial self-sustained extra torques, either with or without the background 20-Hz electrical stimulation applied simultaneously with the vibration. The extra torque generation was accompanied by increased motoneuron excitability, since an increase in the peak-to-peak amplitude of soleus F waves was observed. The delivery of electrical stimulation following the vibration was essential to keep the maintained extra torques and increased F-waves.</p> <p>Conclusions</p> <p>These results show that vibratory stimuli applied with a background electrical stimulation generate considerable force levels (up to about 50% MVC) due to the spinal recruitment of motoneurons. The association of vibration and electrical stimulation could be beneficial for many therapeutic interventions and vibration-based exercise programs. The command for the vibration-induced extra torques presumably activates spinal motoneurons following the size principle, which is a desirable feature for stimulation paradigms.</p

A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties.

INTRODUCTION: Surface electromyography (sEMG) is the measurement of the electrical activity of the skeletal muscle tissue detected at the skin's surface. Typically, a bipolar electrode configuration is used. Most muscles have pennate and/or curved fibres, meaning it is not always feasible to align the bipolar electrodes along the fibres direction. Hence, there is a need to explore how different electrode designs can affect sEMG measurements. METHOD: A three layer finite element (skin, fat, muscle) muscle model was used to explore different electrode designs. The implemented model used as source signal an experimentally recorded intramuscular EMG taken from the biceps brachii muscle of one healthy male. A wavelet based intensity analysis of the simulated sEMG signal was performed to analyze the power of the signal in the time and frequency domain. RESULTS: The model showed muscle tissue causing a bandwidth reduction (to 20-92- Hz). The inter-electrode distance (IED) and the electrode orientation relative to the fibres affected the total power but not the frequency filtering response. The effect of significant misalignment between the electrodes and the fibres (60°- 90°) could be reduced by increasing the IED (25-30 mm), which attenuates signal cancellation. When modelling pennated fibres, the muscle tissue started to act as a low pass filter. The effect of different IED seems to be enhanced in the pennated model, while the filtering response is changed considerably only when the electrodes are close to the signal termination within the model. For pennation angle greater than 20°, more than 50% of the source signal was attenuated, which can be compensated by increasing the IED to 25 mm. CONCLUSION: Differences in tissue filtering properties, shown in our model, indicates that different electrode designs should be considered for muscle with different geometric properties (i.e. pennated muscles)

Age-dependent motor unit remodelling in human limb muscles.

Voluntary control of skeletal muscle enables humans to interact with and manipulate the environment. Lower muscle mass, weakness and poor coordination are common complaints in older age and reduce physical capabilities. Attention has focused on ways of maintaining muscle size and strength by exercise, diet or hormone replacement. Without appropriate neural innervation, however, muscle cannot function. Emerging evidence points to a neural basis of muscle loss. Motor unit number estimates indicate that by age around 71 years, healthy older people have around 40 % fewer motor units. The surviving low- and moderate-threshold motor units recruited for moderate intensity contractions are enlarged by around 50 % and show increased fibre density, presumably due to collateral reinnervation of denervated fibres. Motor unit potentials show increased complexity and the stability of neuromuscular junction transmissions is decreased. The available evidence is limited by a lack of longitudinal studies, relatively small sample sizes, a tendency to examine the small peripheral muscles and relatively few investigations into the consequences of motor unit remodelling for muscle size and control of movements in older age. Loss of motor neurons and remodelling of surviving motor units constitutes the major change in ageing muscles and probably contributes to muscle loss and functional impairments. The deterioration and remodelling of motor units likely imposes constraints on the way in which the central nervous system controls movements

Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models

<div><p>This paper introduces a new method for data analysis of animal muscle activation during locomotion. It is based on fitting Gaussian mixture models (GMMs) to surface EMG data (sEMG). This approach enables researchers/users to isolate parts of the overall muscle activation within locomotion EMG data. Furthermore, it provides new opportunities for analysis and exploration of sEMG data by using the resulting Gaussian modes as atomic building blocks for a hierarchical clustering. In our experiments, composite peak models representing the general activation pattern per sensor location (one sensor on the long back muscle, three sensors on the gluteus muscle on each body side) were identified per individual for all 14 horses during walk and trot in the present study. Hereby we show the applicability of the method to identify composite peak models, which describe activation of different muscles throughout cycles of locomotion.</p></div