Vojko Glaser

Non-invasive characterization of motor unit behaviour in pathological tremor

This paper presents the fully automatic identification of motor unit spike trains from high-density surface electromyograms (EMG) in pathological tremor. First, a mathematical derivation is provided to theoretically prove the possibility of decomposing noise-free high-density surface EMG signals into motor unit spike trains with high correlation, which are typical of tremor contractions. Further, the proposed decomposition method is tested on simulated signals with different levels of noise and on experimental signals from 14 tremor-affected patients. In the case of simulated tremor with central frequency ranging from 5 Hz to 11 Hz and signal-to-noise ratio of 20 dB, the method identified ∼8 motor units per contraction with sensitivity in spike timing identification ≥ 95% and false alarm and miss rates ≤ 5%. In experimental signals, the number of identified motor units varied substantially (range 0-21) across patients and contraction types, as expected. The behaviour of the identified motor units was consistent with previous data obtained by intramuscular EMG decomposition. These results demonstrate for the first time the possibility of a fully non-invasive investigation of motor unit behaviour in tremor-affected patients. The method provides a new means for physiological investigations of pathological tremor.

Real-Time Motor Unit Identification From High-Density Surface EMG

This study addresses online decomposition of high-density surface electromyograms (EMG) in real time. The proposed method is based on the previously published Convolution Kernel Compensation (CKC) technique and shares the same decomposition paradigm, i.e., compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires ~ 10 s of EMG signal, the real-time implementation begins with batch processing of ~ 3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal linear minimum mean square error (LMMSE) estimator demonstrates high agreement in identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98%. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90% of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.

The Phase Difference Between Neural Drives to Antagonist Muscles in Essential Tremor Is Associated with the Relative Strength of Supraspinal and Afferent Input

The pathophysiology of essential tremor (ET), the most common movement disorder, is not fully understood. We investigated which factors determine the variability in the phase difference between neural drives to antagonist muscles, a long-standing observation yet unexplained. We used a computational model to simulate the effects of different levels of voluntary and tremulous synaptic input to antagonistic motoneuron pools on the tremor. We compared these simulations to data from 11 human ET patients. In both analyses, the neural drive to muscle was represented as the pooled spike trains of several motor units, which provides an accurate representation of the common synaptic input to motoneurons. The simulations showed that, for each voluntary input level, the phase difference between neural drives to antagonist muscles is determined by the relative strength of the supraspinal tremor input to the motoneuron pools. In addition, when the supraspinal tremor input to one muscle was weak or absent, Ia afferents provided significant common tremor input due to passive stretch. The simulations predicted that without a voluntary drive (rest tremor) the neural drives would be more likely in phase, while a concurrent voluntary input (postural tremor) would lead more frequently to an out-of-phase pattern. The experimental results matched these predictions, showing a significant change in phase difference between postural and rest tremor. They also indicated that the common tremor input is always shared by the antagonistic motoneuron pools, in agreement with the simulations. Our results highlight that the interplay between supraspinal input and spinal afferents is relevant for tremor generation.

Noninvasive analysis of motor unit behavior in pathological tremor

A robust surface EMG decomposition tool, referred to as tremor-optimized Convolution Kernel Compensation (CKC) technique, is described. This technique modifies and extends the previously published CKC method in order to circumvent the typical assumption on regularity and asynchrony of motor unit firings in normal condition and adapt to the discharge patterns in pathological tremor. The results on synthetic and experimental surface EMG signals demonstrate high performance of decomposition. In the case of simulated surface EMG with 20 dB SNR, excitation level of 20% maximum voluntary contraction (MVC) and simulated tremor frequency of 8 Hz, the newly proposed method identified 8 ± 2 motor units with sensitivity of motor unit discharge identification > 95 % and false alarm and miss rates < 5%. The performance worsened with increasing noise power, with 5 ± 2 motor units identified at 10 dB SNR and 3 ± 1 at 0 dB SNR. In 24 recordings of high-density surface EMG signals from four tremor-affected patients, the modified CKC technique identified 134 motor units (6 ± 4 motor units per contraction).

Impact of motor unit action potential components on the motor unit identification from dynamic high-density surface electromyograms

We assessed the impact of different motor unit action potential (MUAP) components in dynamic muscle contractions on decomposition of high-density surface electromyograms (hdEMG). In particular, hypothesis that nontravelling MUAP components, originating from the tendon regions, are less sensitive to changes in geometry of fusiform muscles than travelling MUAP components has been tested on synthetic monopolar hdEMG signals. The latter have been decomposed by previously introduced Convolution Kernel Compensation (CKC) method, using five different sections of simulated MUAPs for motor unit identification. Accuracy of decomposition results increased significantly when motor units were identified from the nontravelling MUAP components, compared to the results obtained from travelling components. Average motor unit identification sensitivity increased from 67.4%±15.7% to 81.3%±11.3% and false alarm rate decreased from 0.75% ± 1.21% to 0.20% ± 0.24%. Results confirmed that non-travelling MUAP components are discriminative enough to reliably identify motor units from hdEMG and less sensitive to geometric changes of fusiform muscles during dynamic muscle contractions than travelling MUAP components.

New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor

Pathological tremor is a common but highly complex movement disorder, affecting ~5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson’s disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson’s disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.

Motor unit-driven identification of pathological tremor in electroencephalograms

Background: Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordings, and it is sensitive to EEG artifacts. Here, we analytically derive and experimentally validate a new method for automatic extraction of the tremor-related EEG component in pathological tremor patients that aims to overcome these limitations. Methods: We exploit the coupling between the tremor-related cortical activity and motor unit population firings to build a linear minimum mean square error estimator of the tremor component in EEG. We estimated the motor unit population activity by decomposing surface EMG signals into constituent motor unit spike trains, which we summed up into a cumulative spike train (CST). We used this CST to initialize our tremor-related EEG component estimate, which we optimized using a novel approach proposed here. Results: Tests on simulated signals demonstrate that our new method is robust to both noise and motor unit firing variability, and that it performs well across a wide range of spectral characteristics of the tremor. Results on 9 essential (ET) and 9 Parkinsons disease (PD) patients show a ~2-fold increase in amplitude of the coherence between the estimated EEG component and the CST, compared to the classical EEG-EMG coherence analysis. Conclusions: We have developed a novel method that allows for more precise and robust estimation of the tremor-related EEG component. This method does not require artifact removal, provides reliable results in relatively short datasets, and tracks changes in the tremor-related cortical activity over time.

Simulations of high-density surface electromyograms in dynamic muscle contractions

We describe the extension of pre-existing cylindrical volume conductor model to synthetic high-density surface electromyograms (hdEMG), simulated during dynamic contractions of fusiform skeletal muscles. Its modular structure comprises two main parts. First, dynamic changes of motor unit action potentials (MUAPs) during 36 discrete steps of muscle shortening are simulated. Second, the increase in depth of simulated motor units (MUs) due to shortening and thickening of muscle fibers is simulated. MU firing patterns are generated with the model proposed by Fuglevand et al. and convolved with simulated MUAPs. In this way, the hdEMG simulator can be used to generate dynamic hdEMG of arbitrary muscle shortening, thickening and excitation profiles. In order to demonstrate the value of the aforementioned simulator we independently analyze the impact of muscle shortening and muscle thickening on MU identification by Convolution Kernel Compensation (CKC) technique.

A Novel Measure of Motor Unit Action Potential Variability in Nonstationary Surface Electromyograms

We introduce and validate a novel measure of motor unit action potential (MUAP) variability in surface electromyograms (EMG) that are recorded during dynamic muscle contractions. This measure is fully automatic, builds on the motor unit spike trains as estimated by previously introduced Convolution Kernel Compensation method and allows tracking of MUAP variability for each individual motor unit separately. Preliminary tests on synthetic surface EMG signals demonstrate its high accuracy and capability of identifying cyclostationary changes of MUAP shapes. This measure represents the first, but very important step towards motor unit identification in dynamic muscle contractions.

Decomposition of surface electromyograms reveals changes in motor control after 14-day bed rest

We demonstrate the advantages of motor unit (MU) identification from noninvasively recorded high-density surface electromyograms (EMG) of gastrocnemius medialis, gastrocnemius lateralis and soleus muscles for detailed analysis of motor control changes after two weeks of bed rest. Five young (18-28 years) and five older (53-65 years) healthy subjects participated in the study. High-density surface electromyograms of both gastrocnemii and soleus muscles were recorded in the non-dominant (left) leg before (pre) and immediately after (post) the bed rest. The signals were decomposed by Convolution Kernel Compensation method into contributions of individual MUs and the MU discharge rates in both recording sessions were mutually compared. On average, 22.6 ± 12.4 and 8.3 ± 6.9 motor units have been accurately identified in each gastrocnemius muscle of older and young subjects, respectively. In the soleus muscle these numbers were lower, i.e. 15.1 ± 7.8 and 4.3 ± 2.4 motor units for older and young subjects, respectively. In all the investigated muscles the recruitment thresholds of identified MUs were uniformly distributed over the investigated force ranges from 0 % to 60 % of maximum voluntary contraction (MVC). The only exceptions were gastrocnemii muscles of older subjects, where after the bed rest more than 70 % of identified MUs were recruited below 20 % of MVC. Principal component analysis of smoothed MU discharge rates showed statistically significant decrease of MU discharge rates and decrease of their common modulation after the bed rest in all investigated muscles of young subjects and in soleus muscle of older adults. No significant changes were observed in gastrocnemii muscles of older subjects. It was concluded that high-density surface EMG offers a detailed insight into the alternations of motor control strategies and is sensitive enough to detect and characterize the changes caused by a relatively short bed rest protocol.