Analysis of motor unit firing patterns in patients with central or peripheral lesions using singular-value decomposition

Improved online decomposition of non-stationary electromyogram via signal enhancement using a neuron resonance model: a simulation study

Objective. Motor unit (MU) discharge information obtained via the online electromyogram (EMG) decomposition has shown promising prospects in multiple applications. However, the nonstationarity of EMG signals caused by the rotation (recruitment-derecruitment) of MUs and the variation of MU action potentials (MUAP) can significantly degrade the online decomposition performance. This study aimed to develop an independent component analysis-based online decomposition method that can accommodate the nonstationarity of EMG signals.Approach. The EMG nonstationarity can make the separation vectors obtained beforehand inaccurate, resulting in the reduced amplitudes of the peaks corresponding to firing events in the source signal (independent component) and then the decreased accuracy of firing events. Therefore, we utilized the FitzHugh-Nagumo (FHN) resonance model to enhance the firing peaks in the source signal in order to improve the decomposition accuracy. A two-session approach was used with the offline session to extract the separation vectors and train the FHN models. In the online session, the source signal was estimated and further processed using the FHN model before detecting the firing events in a real-time manner. The proposed method was tested on simulated EMG signals, in which MU rotation and MUAP variation were involved to mimic the nonstationarity of EMG recordings.Main results. Compared with the conventional method, the proposed method can improve the decomposition accuracy significantly (88.70% ± 4.17% vs. 92.43% ± 2.79%) by enhancing the firing peaks, and more importantly, the improvement was more prominent when the EMG signal had stronger background noises (87.00% ± 3.70% vs. 91.66% ± 2.63%).Conclusions. Our results demonstrated the effectiveness of the proposed method to utilize the FHN model to improve the online decomposition performance on the nonstationary EMG signals. Further development of our method has the potential to improve the performance of the neural decoding system that utilizes the MU discharge information and promote its application in the neural-machine interface.

Needle Electromyography Waveforms During Needle Electromyography

Needle electromyography (EMG) waveforms recorded during needle EMG help to define the type, temporal course, and severity of a neuromuscular disorder. Accurate interpretation of EMG waveforms is a critical component of an electrodiagnostic examination. This article reviews the significance of spontaneous EMG waveforms and changes in voluntary motor unit potentials in neuromuscular disorders.

Association between the Degree of Pre-Synaptic Dopaminergic Pathway Degeneration and Motor Unit Firing Behavior in Parkinson's Disease Patients

The relationship between motor unit (MU) firing behavior and the severity of neurodegeneration in Parkinson’s disease (PD) is not clear. This study aimed to elucidate the association between degeneration with dopaminergic pathways and MU firing behavior in people with PD. Fourteen females with PD (age, 72.6 ± 7.2 years, disease duration, 3.5 ± 2.1 years) were enrolled in this study. All participants performed a submaximal, isometric knee extension ramp-up contraction from 0% to 80% of their maximal voluntary contraction strength. We used high-density surface electromyography with 64 electrodes to record the muscle activity of the vastus lateralis muscle and decomposed the signals with the convolution kernel compensation technique to extract the signals of individual MUs. We calculated the degree of degeneration of the central lesion-specific binding ratio by dopamine transporter single-photon emission computed tomography. The primary, novel results were as follows: (1) moderate-to-strong correlations were observed between the degree of degeneration of the central lesion and MU firing behavior; (2) a moderate correlation was observed between clinical measures of disease severity and MU firing behavior; and (3) the methods of predicting central nervous system degeneration from MU firing behavior abnormalities had a high detection accuracy with an area under the curve >0.83. These findings suggest that abnormalities in MU activity can be used to predict central nervous system degeneration following PD.

Adaptive Real-time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study

OBJECTIVE

Real-time decomposition of electromyogram (EMG) into constituent motor unit (MU) activity has shown promising applications in neurophysiology and human-machine interactions. Existing decomposition methods could not accommodate stochastic variations in EMG signals such as drifts of action potential amplitudes and MU recruitment-derecruitment (rotation) patterns during long-term recordings. The objective of this study was to develop an adaptive real-time decomposition approach suitable for prolonged muscle activation.

METHODS

We developed a parallel-double-thread computation algorithm. The backend thread initiated and periodically refined and updated the MU information (separation matrix) using independent component analysis and convolution kernel compensation. The frontend thread performed the real-time decomposition. We evaluated our algorithm on synthesized high-density EMG signals, in which MUs were recruited-derecruited sporadically and MU action potentials amplitude drifted over time. Different signal-to-noise levels were also simulated.

RESULTS

Compared with the decomposition without the adaptive processes, periodically fine-tuned and updated separation matrix increased identifiable MU number by 3-4 fold over 30-minute of recordings. The increased MU number was more prominent at higher signal-to-noise ratios. The decomposition accuracy also increased by up to 10% with greater improvement observed at higher muscle contraction levels.

CONCLUSION

The adaptive algorithm can maintain the decomposition performance over time, allows us to continuously track the same MUs during sustained activation, and, at the same time, can add newly recruited MU information to existing separation matrix.

SIGNIFICANCE

Our approach showed robust performance over time, which has the potential to longitudinally evaluate MU firing and recruitment properties and improve neural decoding performance for neural-machine interactions.

Identification of the laterality of motor unit behavior in female patients with parkinson's disease using high-density surface electromyography

Patients with Parkinson's disease (PD) have greater laterality of muscle contraction properties than other people with parkinsonism diseases. However, few studies have reported the laterality of MU activation properties of the lower extremity muscles in patients with PD. The aim of the present study was to identify the laterality of MU behavior in PD patients using high-density surface electromyography (HD-SEMG). Eleven female patients with PD (age, 69.2 ± 6.2 years, disease duration, 2.7 ± 0.9 years, Unified Parkinson's disease Rating Scale score, 13 (9-16)), and 9 control female subjects (age, 66.8 ± 3.5 years) were enrolled in the present study. All subjects performed a sustained isometric knee extension in a 30% maximal voluntary contraction (MVC) task for 20 s. HD-SEMG signals were used to record and extract single MU firing behavior in the vastus lateralis muscle during submaximal isometric knee extensor contractions with 64 electrodes and decomposed with the convolution kernel compensation technique to extract individuals MUs. Compared to the control subjects, the patients with PD exhibited laterality of the MU firing rate and an absence of a relationship between the mean MU firing rate and MU threshold. Patients with PD exhibit laterality of MU behavior and experience MU behavioral abnormalities even with mild symptoms such as Hoehn & Yahr stage ≤ 3 and disease duration = 2.7 ± 0.9. These findings suggest the importance of considering the detection of abnormal muscle properties in PD patients beginning in the early phase of the disease.

Normal and abnormal voluntary activity

An important component of needle EMG entails recording and interpreting the electrical signals generated from motor units during voluntary contraction. The recorded motor unit potentials (MUPs) reflect the number of motor units within a muscle and the distribution and density of muscle fibers within a motor unit within a portion of a muscle. Various MUP parameters are assessed to determine the integrity of the motor units, including recruitment, stability, phases and turns, duration, and amplitude. Each of these parameters is altered in a different way in various neuromuscular diseases. In neurogenic disorders, the earliest changes occur in the recruitment pattern of motor units followed by MUP morphologic changes (increased MUP phases and duration) as reinnervation occurs. MUP instability, indicating impaired neuromuscular transmission, also occurs in reinnervating neurogenic disorders as well as in neuromuscular junction disorders. In myopathies, a reduction in the size of the motor unit is manifested by MUPs of low amplitude and short duration. Interpreting the voluntary MUP changes along with spontaneous activity helps to determine the type, severity, and temporal course of neuromuscular diseases.

Bilateral changes in afterhyperpolarization duration of spinal motoneurones in post-stroke patients

This paper extends the observations presented in the previously published work on the afterhyperpolarization (AHP) duration changes in motoneurones (MNs) on the paretic (more affected) side of 11 post-stroke patients by the same analysis on the non-paretic (less-affected) side. The estimated AHP duration for patients’ MNs supplying more-affected muscles was significantly longer than control values and the elongation decreased with patient age and disorder duration. For MNs supplying less-affected muscles, dependency of AHP duration on age was closer to the control data, but the scatter was substantially bigger. However, the AHP duration estimate of less-affected MNs tended to be longer than that of controls in the short time elapsed since the stroke, and shorter than controls in the long time. Our results thus suggest that the spinal MNs on both sides respond to the cerebral stroke rapidly with prolongation of AHP duration, which tends to normalize with time, in line with functional recovery. This suggestion is in concert with the published research on post-stroke changes in brain hemispheres. To our knowledge, these dependencies have never been investigated before. Since the number of our data was limited, the observed trends should be verified in a larger sample of patients and such a verification could take into account the suggestions for data analysis that we provide in this paper. Our data are in line with the earlier published research on MN firing characteristics post-stroke and support the conclusion that the MUs of the muscles at the non-paretic side are also affected and cannot be considered a suitable control for the MUs on the paretic side.

Adaptive motor unit potential train validation using MUP shape information

A decomposed electromyographic (EMG) signal provides information that can be used clinically or for physiological investigation. However, in all instances the validity of the extracted motor unit potential trains (MUPTs) must first be determined because, as with all pattern recognition applications, errors will occur during decomposition. Moreover, detecting invalid MUPTs during EMG signal decomposition can enhance decompositions results. Eight methods to validate an extracted MUPT using its motor unit potential (MUP) shape information were studied. These MUPT validation methods are based on existing cluster analysis algorithms, four were newly developed adaptive methods and four were classical cluster validation methods. The methods evaluate the shapes of the MUPs of a MUPT to determine whether the MUPT represents the activity of a single motor unit (i.e. it is a valid MUPT) or not. Evaluation results using both simulated and real data show that the newly developed adaptive methods are sufficiently fast and accurate to be used during or after the decomposition of EMG signals. The adaptive gap-based Duda and Hart (AGDH) method had significantly better accuracies in correctly categorizing the MUPTs extracted during decomposition (91.3% and 94.7% for simulated and real data, respectively; assuming 12.7% of the extracted MUPTs are on average invalid). The accuracy with which invalid MUPTs can be detected is dependent on the similarity of the MUP templates of the MUPTs merged to create the invalid train and suggests the need, in some cases, for the combined use of motor unit firing pattern and MUP shape information.

Validating motor unit firing patterns extracted by EMG signal decomposition

Motor unit (MU) firing pattern information can be used clinically or for physiological investigation. It can also be used to enhance and validate electromyographic (EMG) signal decomposition. However, in all instances the validity of the extracted MU firing patterns must first be determined. Two supervised classifiers that can be used to validate extracted MU firing patterns are proposed. The first classifier, the single/merged classifier (SMC), determines whether a motor unit potential train (MUPT) represents the firings of a single MU or the merged activity of more than one MU. The second classifier, the single/contaminated classifier (SCC), determines whether the estimated number of false-classification errors in a MUPT is acceptable or not. Each classifier was trained using simulated data and tested using simulated and real data. The accuracy of the SMC in categorizing a train correctly is 99% and 96% for simulated and real data, respectively. The accuracy of the SCC is 84% and 81% for simulated and real data, respectively. The composition of these classifiers, their objectives, how they were trained, and the evaluation of their performances using both simulated and real data are presented in detail.

Validation of motor unit potential trains using motor unit firing pattern information

A robust and fast method to assess the validity of a motor unit potential train (MUPT) obtained by decomposing a needle-detected EMG signal is proposed. This method determines whether a MUPT represents the firings of a single motor unit (MU) or the merged activity of more than one MU, and if is a single train it identifies whether the estimated levels of missed and false classification errors in the MUPT are acceptable. Two supervised classifiers, the Single/Merged classifier (SMC) and the Error Rate classifier (ERC), and a linear model for estimating the level of missed classification error have been developed for this objective. Experimental results using simulated data show that the accuracy of the SMC and the ERC in correctly categorizing a train is 99% and %84 respectively.

Needle electromyography

Physiologic assessment of diseases of the motor unit from the anterior horn cells to the muscles relies on a combination of needle electromyography (EMG) and nerve conduction studies (NCS). Both require a unique combination of knowledge of peripheral nervous system anatomy, physiology, pathophysiology, diseases, techniques, and electricity is necessary. Successful, high-quality, reproducible EMG depends on the skills of a clinician in patient interaction during the physical insertion and movement of the needle while recording the electrical signals. These must be combined with the skill of analyzing electric signals recorded from muscle by auditory pattern recognition and semiquantitation.1052 This monograph reviews the techniques of needle EMG and waveform analysis and describes the types of EMG waveforms recorded during needle EMG.

Using two-dimensional spatial information in decomposition of surface EMG signals

Recently, high-density surface EMG electrode grids and multi-channel amplifiers became available for non-invasive recording of human motor units (MUs). We present a way to decompose surface EMG signals into MU firing patterns, whereby we concentrate on the importance of two-dimensional spatial differences between the MU action potentials (MUAPs). Our method is exemplified with high-density EMG data from the vastus lateralis muscle of a single subject. Bipolar and Laplacian spatial filtering was applied to the monopolar raw signals. From the single recording in this subject six different simultaneously active MUs could be distinguished using the spatial differences between MUAPs in the direction perpendicular to the muscle fiber direction. After spike-triggered averaging, 125-channel two-dimensional MUAP templates were obtained. Template-matching allowed tracking of all MU firings. The impact of spatial information was measured by using subsets of the MUAP templates, either in parallel or perpendicular to the muscle fiber direction. The use of one-dimensional spatial information perpendicular to the muscle fiber direction was superior to the use of a linear array electrode in the longitudinal direction. However, to detect the firing events of the MUs with a high accuracy, as needed for instance for estimation of firing synchrony, two-dimensional information from the complete grid electrode appears essential.

Motor unit discharge pattern and conduction velocity in patients with upper motor neuron syndrome

Motor unit properties were analyzed in patients with upper motor neuron syndrome (UMNS). Multi-channel surface electromyographic (EMG) signals were recorded for 300s from the biceps brachii muscle of seven male subacute patients (time from lesion, mean+/-SE, 4.9+/-1.0 months). In three patients, both arms were investigated, leading to 10 recorded muscles. Patients were analyzed in rest-like condition with motor units activated due to pathological muscle overactivity. For a total of 12 motor units, the complete discharge pattern was extracted from EMG decomposition. Interpulse interval variability was 7.8+/-0.9%. At minimum discharge rate (6.4+/-0.4 pulses per second, pps), conduction velocity was smaller than at maximum discharge rate (12.0+/-0.9pps) in all motor units (3.60+/-0.21m/s vs. 3.84+/-0.20m/s). Conduction velocity changed by 1.35+/-0.48% (different from zero, P<0.01) for each increase of 1pps in discharge rate. It was concluded that conduction velocity of low-threshold motor units in subacute patients with UMNS had similar values as reported in healthy subjects and was positively correlated to instantaneous discharge rate (velocity recovery function of muscle fibers).

Clinical applications of high-density surface EMG: A systematic review

High density-surface EMG (HD-sEMG) is a non-invasive technique to measure electrical muscle activity with multiple (more than two) closely spaced electrodes overlying a restricted area of the skin. Besides temporal activity HD-sEMG also allows spatial EMG activity to be recorded, thus expanding the possibilities to detect new muscle characteristics. Especially muscle fiber conduction velocity (MFCV) measurements and the evaluation of single motor unit (MU) characteristics come into view. This systematic review of the literature evaluates the clinical applications of HD-sEMG. Although beyond the scope of the present review, the search yielded a large number of "non-clinical" papers demonstrating that a considerable amount of work has been done and that significant technical progress has been made concerning the feasibility and optimization of HD-sEMG techniques. Twenty-nine clinical studies and four reviews of clinical applications of HD-sEMG were considered. The clinical studies concerned muscle fatigue, motor neuron diseases (MND), neuropathies, myopathies (mainly in patients with channelopathies), spontaneous muscle activity and MU firing rates. In principle, HD-sEMG allows pathological changes at the MU level to be detected, especially changes in neurogenic disorders and channelopathies. We additionally discuss several bioengineering aspects and future clinical applications of the technique and provide recommendations for further development and implementation of HD-sEMG as a clinical diagnostic tool.

Clinical applications of surface electromyography in neuromuscular disorders

Surface electromyography (SEMG) is still rarely used in clinical settings for the detection and analysis of myoelectric signals. The electromyographic signal detected on the skin surface includes information from a greater proportion of the muscle of interest than conventional clinical EMG, acquired using needle electrodes. SEMG is therefore more representative than the localised, and thus very selective needle EMG signals currently used. However, both reliability and interpretation of surface EMG need to be questioned. This review looks at the studies concerned with the characterisation of neuromuscular pathologies using EMG parameters. After introducing principles and limitations of surface EMG, an overview of the main results obtained in clinical settings is presented and discussed. There is a particular focus on high spatial resolution surface EMG as it is currently the best compromise between the selectivity of needle EMG and the representative nature of classical SEMG. Several perspectives are proposed that underline the fact that surface EMG is an evolving discipline and should be worthy of a place in routine clinical examinations.