Decomposition of intramuscular EMG signals using a knowledge -based certainty classifier algorithm

Controversy persists regarding the presence and importance of hypoglossal nerve dysfunction in obstructive sleep apnea (OSA). We assessed quantitative parameters related to motor unit potential (MUP) morphology derived from electromyographic (EMG) signals in patients with OSA versus control subjects and hypothesized that signs of neurogenic remodeling would be present in the patients with OSA. Participants underwent diagnostic sleep studies to obtain apnea-hypopnea indices. Muscle activity was detected with 50-mm concentric needle electrodes. The concentric needle was positioned at more than 10 independent sites per subject, after the local anatomy of the upper airway musculature was examined by ultrasonography. All activity was quantified with subjects awake, during supine eupneic breathing while wearing a nasal mask connected to a pneumotachograph. Genioglossus EMG signals were analyzed offline by automated software (DQEMG), which extracted motor unit potential trains (MUPTs) contributed by individual motor units from the composite EMG signals. Quantitative measurements of MUP templates, including duration, peak-to-peak amplitude, area, area-to-amplitude ratio, and size index, were compared between the untreated patients with OSA and healthy control subjects. A total of 1,655 MUPTs from patients with OSA (n = 17; AHI, 55 ± 6/h) and control subjects (n = 14; AHI, 4 ± 1/h) were extracted from the genioglossus muscle EMG signals. MUP peak-to-peak amplitudes in the patients with OSA were not different compared with the control subjects (397.5 ± 9.0 vs. 382.5 ± 10.0 μV). However, the MUPs of the patients with OSA were longer in duration (11.5 ± 0.1 vs. 10.3 ± 0.1 ms; P < 0.001) and had a larger size index (4.09 ± 0.02 vs. 3.92 ± 0.02; P < 0.001) compared with control subjects. These results confirm and quantify the extent and existence of structural neural remodeling in OSA.

P36-S Validation of near fiber motor unit potential stability and dispersion measures using simulated signals

EMGLAB: An interactive EMG decomposition program

The purpose of this study was to determine if the implementation of a strict validation procedure, designed to limit the inclusion of inaccuracies from the decomposition of surface electromyographic (sEMG) signals, affects population-based motor unit (MU) analyses. Four sEMG signals were obtained from the vastus lateralis of 59 participants during isometric contractions at different relative intensities [30%, 70%, and 100% of maximal voluntary contraction (MVC)], and its individual motor unit potential trains (MUPTs) were extracted. The MUPTs were then excluded (ISIval) based on the coefficient of variation and histogram of the interspike intervals (ISI), the absence of additional clusters that reveals missed or additional firings, and more. MU population-based regression models (i.e., modeling the entire motor unit pool) were performed between motor unit potential size (MUPSIZE), mean firing rate (MFR), and recruitment threshold (RT%) separately for DSDCOnly (includes all MUPTs without the additional validation performed) and ISIval data at each contraction intensity. The only significant difference in regression coefficients between DSDCOnly and ISIval was for the intercepts of the MUPSIZE/MFR at 100% MVC. The validation had no other significant effect on any of the other regression coefficients for each of the contraction intensities. Our findings suggest that even though the decomposition of surface signals leads to some inaccuracies, these errors have limited effects on the regression models used to estimate the behavior of the whole pool. Therefore, we propose that motor unit population-based regression models may be robust enough to overcome decomposition-induced errors at the individual MU level.

EP 18. Normative values for motor unit potentials in the erector trunci muscle determined by concentric needle electromyography

Non-parametric classification procedures based on a certainty measure and nearest neighbour rule for motor unit potential classification (MUP) during electromyographic (EMG) signal decomposition were explored. A diversity-based classifier fusion approach is developed and evaluated to achieve improved classification performance. The developed system allows the construction of a set of non-parametric base classifiers and then automatically chooses, from the pool of base classifiers, subsets of classifiers to form candidate classifier ensembles. The system selects the classifier ensemble members by exploiting a diversity measure for selecting classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between base classifier outputs, i.e., to measure the degree of decision similarity between base classifiers. The pool of base classifiers consists of two kinds of classifiers: adaptive certainty-based classifiers (ACCs) and adaptive fuzzy k-NN classifiers (AFNNCs) and both utilize different types of features. Once the patterns are assigned to their classes, by the classifier fusion system, firing pattern consistency statistics for each class are calculated to detect classification errors in an adaptive fashion. Performance of the developed system was evaluated using real and simulated EMG signals and was compared with the performance of the constituent base classifiers and the performance of the fixed ensemble containing the full set of base classifiers. Across the EMG signal data sets used, the diversity-based classifier fusion approach had better average classification performance overall, especially in terms of reducing classification errors.

Advances in surface electromyographic (EMG) signal decomposition now allow investigators to analyze firing rate data for 20-50 motor units per contraction. To simplify data interpretation, some investigators have relied on group mean analysis of the mean firing rate versus recruitment threshold relationship. It is unclear, however, whether this association is consistently linear. PURPOSE: To determine whether the motor unit mean firing rate versus recruitment threshold relationship is strongest when analyzed via linear, quadratic, or cubic regression. METHODS: Twenty-one men (mean ± SD age = 24 ± 4 years) participated in this study. After determining maximal voluntary contraction (MVC) force of the dominant knee extensors, participants performed trapezoidal isometric contractions at 50% MVC by tracing a visual template displayed on a monitor directly in front of them. Participants were instructed to increase force from 0 to 50% in five seconds, maintain 50% MVC for 15 seconds, and decrease force from 50% to 0 in five seconds. Bipolar surface EMG signals were recorded from the vastus lateralis during each contraction. A surface EMG signal decomposition algorithm was used to calculate mean firing rate and recruitment threshold of each detected motor unit. Motor units with decomposition accuracy levels <90% were discarded. Polynomial regression was used to determine if each mean firing rate versus recruitment threshold relationship was best fit with a linear, quadratic, or cubic model. Data were interpreted on an individual participant basis. RESULTS: Statistically significant (p <.05) moderate to strong (r2=.599-.964) linear relationships existed for all 21 participants. Fourteen of the 21 participants demonstrated relationships that were best fit with a linear model (r2=.599-.964). Of the remaining seven participants, five were best fit with a quadratic model (r2=.864-.953) and two were best fit with a cubic model (r2=.977-.989). CONCLUSION: While moderate to strong linear relationships were found between the firing rate of motor units and their recruitment thresholds for all participants, in certain cases the strength of the association was enhanced when analyzed via a non-linear model. Our findings provide further support for the need to examine motor unit data on a participant-by-participant basis.

BackgroundStudying the changes that occur in motor unit potential trains (MUPTs) may provide insight into the extent of motor unit loss and neural re-organization resulting from nerve compression injury. The purpose of this study was to determine the feasibility of using decomposition-based quantitative electromyography (DQEMG) to study the pathophysiological changes associated with compression neuropathy.MethodsThe model used to examine compression neuropathy was carpal tunnel syndrome (CTS) due to its high prevalence and ease of diagnosis. Surface and concentric needle electromyography data were acquired simultaneously from the abductor pollicis brevis muscle in six individuals with severe CTS, eight individuals with mild CTS and nine healthy control subjects. DQEMG was used to detect intramuscular MUPTs during constant-intensity contractions and to estimate parameters associated with the surface- and needle-detected motor unit potentials (SMUPs and MUPs, respectively). MUP morphology and stability, SMUP morphology and motor unit number estimates (MUNEs) were compared among the groups using Kruskal-Wallis tests.ResultsThe severe CTS group had larger amplitude and longer duration MUPs and smaller MUNEs than the mild CTS and control groups, suggesting that the individuals with severe CTS had motor unit loss with subsequent collateral reinnervation, and that DQEMG using a constant-intensity protocol was sensitive to these changes. SMUP morphology and MUP complexity and stability did not significantly differ among the groups.ConclusionsThese results provide evidence that MUP amplitude parameters and MUNEs obtained using DQEMG, may be a valuable tool to investigate pathophysiological changes in muscles affected by compressive motor neuropathy to augment information obtained from nerve conduction studies. Although there were trends in many of these measures, in this study, MUP complexity and stability and SMUP parameters were, of limited value.

Decomposition and quantitative analysis of clinical electromyographic signals

&lt;p&gt;The shapes and firing rates of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from EMG signals recorded at force levels up to 20% of maximum voluntary contraction (MVC) it is required: (i) To identify the MUAPs composing the EMG signal, (ii) To classify MUAPs with similar shape and (iii) To decompose the superimposed MUAP waveforms into their constituent MUAPs. For the classification of MUAPs two different pattern recognition techniques are presented (i) An artificial neural network (ANN) technique based on unsupervised learning using the self-organizing feature maps (SOFM) algorithm and learning vector quantization (LVQ) and (ii) A statistical pattern recognition technique based on the euclidian distance. The success rate on real data for the ANN technique is about 96% and for the statistical one about 94%. For the decomposition of the superimposed waveforms the following technique is used: (i) Cross-correlation of each of the unique MUAP waveforms, obtained by the classification process with the superimposed waveforms in order to find the best matching point and (ii) A combination of euclidian distance and area measures in order to classify the components of the decomposed waveform. The success rate for the decomposition procedure is about 90%.&lt;/p&gt;

The clinical community has long shown interest in the concept of extracting as many motor unit action potentials (MUAPs) as possible from an intramuscular electromyographic (EMG) signal. Adrian and Bronk (1929) developed the first concentric needle electrode to identify both shape and firing rate of the MUAPs. Subsequent manual approaches of graphically measuring and quantifying the EMG signal evolved into computer-based techniques directed at identifying individual action potentials and discharge times by shape discrimination. The Precision Decomposition technique described in this chapter recovers all the usable information available in the EMG signal. The information can be conveniently grouped into two categories: morphology and control properties. Morphology describes the parameters of the MUAP shape such as the peak-to-peak amplitude, the time duration, the number of phases, and the area. These parameters are provided by the recovered Concentric and Macro MUAP. The morphology of the MUAP describes features that are related to the anatomical and physiological properties of the muscle fibers. These are the parameters which the clinician is accustomed to evaluating during a standard clinical EMG examination. The control properties of the motor units dictate the firing characteristics of the motor units. Therefore, the firing characteristics provide a description of how the motor units are controlled by the central nervous system and to some extent the peripheral nervous system. Clinically, they quantify upper motoneuron diseases.

Identification of the innervation zone is widely used to optimize the accuracy and precision of noninvasive surface electromyography (EMG) signals because the EMG signal is strongly influenced by innervation zones. However, simply structured fusiform muscle, such as biceps brachii muscle, has been employed mainly due to the simplicity with which the propagation from raw EMG signals can be observed. In this study, the optimum electrode location (OEL), free from innervational influence, was investigated by the propagation pattern of action potentials for brachii muscles and more complicated deltoid muscle structures using an automatized signal analysis technique. The technique employed newly developed computer software with additional clinical uses and minimized subjective differences. EMG signals were recorded using surface array electrodes during voluntary isometric contractions obtained from 12 healthy male subjects. Peaks in EMG signals were detected and averaged for each muscle. The propagation patterns and OEL were examined from biceps brachii muscles for all subjects and from deltoid muscles for seven subjects. The estimated locations were partially confirmed by comparing the root mean squares of the EMG signals. These results show that propagation patterns and OEL could be estimated simply and automatically even from the surface EMG signals of deltoid muscles.

Clinicians who use electromyographic (EMG) signals to help determine the presence or absence of abnormality in a muscle often, with varying degrees of success, evaluate sets of motor unit potentials (MUPs) qualitatively and/or quantitatively to characterize the muscle in a clinically meaningful way. The resulting muscle characterization can be improved using automated analysis. As such, the intent of this study was to evaluate the performance of automated, conventional Means/Outlier and Probabilistic methods in converting MUP statistics into a concise, and clinically relevant, muscle characterization. Probabilistic methods combine the set of MUP characterizations, derived using Pattern Discovery (PD), of all MUPs detected from a muscle into a characterization measure that indicates normality or abnormality. Using MUP data from healthy control subjects and patients with known neuropathic disorders, a Probabilistic method that used Bayes' rule to combine MUP characterizations into a Bayesian muscle characterization (BMC) achieved a categorization accuracy of 79.7% compared to 76.4% using the Mean method (P > 0.1) for biceps muscles and 94.6% accuracy for the BMC method compared to 85.8% using the Mean method (P < 0.01) for first dorsal interosseous muscles. The BMC method can facilitate the determination of "possible," "probable," or "definite" levels for a given muscle categorization (e.g., neuropathic) whereas the conventional Means and Outlier methods support only a dichotomous "normal" or "abnormal" decision. This work demonstrates that the BMC method can provide information that may be more useful in supporting clinical decisions than that provided by the conventional Means or Outlier methods.

Motor unit (MU) expansion enables rescue of denervated muscle fibres helping to ameliorate age-related muscle atrophy, with evidence to suggest master athletes are more successful at this remodelling. Electrophysiological data has suggested MUs located superficially are larger than those located deeper within young muscle. However, the effects of ageing and exercise on MU heterogeneity across deep and superficial aspects of vastus lateralis (VL) remain unclear. Intramuscular electromyography was used to record individual MU potentials (MUPs) and near fibre MUPs (NFMs) from deep and superficial regions of the VL during 25% maximum voluntary contractions, in 83 males (15 young (Y), 17 young athletes (YA), 22 old (O) and 29 master athletes (MA)). MUP size and complexity were assessed using area and number of turns, respectively. Multilevel mixed effects linear regression models were performed to investigate the effects of depth in each group. MUP area was greater in deep compared with superficial MUs in Y (p<0.001) and O (p=0.012) but not in YA (p=0.071) or MA (p=0.653). MUP amplitude and NF MUP area were greater, and MUPs were more complex in deep MUPs from Y, YA and O (all p<0.05) but did not differ across depth in MA (all p>0.07). These data suggest MU characteristics differ according to depth within the VL which may be influenced by both ageing and exercise. A more homogenous distribution of MUP size and complexity across muscle depths in older athletes may be a result of a greater degree of age-related MU adaptations.

Objective. The identification of common oscillatory inputs to motor neurons in the electromyographic (EMG) signal power spectrum is often preceded by EMG rectification for enhancing the low-frequency oscillatory components. However, rectification is a nonlinear operator and its influence on the EMG signal spectrum is not fully understood. In this study, we aim at determining when EMG rectification is beneficial in the study of oscillatory inputs to motor neurons. Approach. We provide a full mathematical description of the power spectrum of the rectified EMG signal and the influence of the average shape of the motor unit action potentials on it. We also provide a validation of these theoretical results with both simulated and experimental EMG signals. Main results. Simulations using an advanced computational model and experimental results demonstrated the accuracy of the theoretical derivations on the effect of rectification on the EMG spectrum. These derivations proved that rectification is beneficial when assessing the strength of low-frequency (delta and alpha bands) common synaptic inputs to the motor neurons, when the duration of the action potentials is short, and when the level of cancellation is relatively low. On the other hand, rectification may distort the estimation of common synaptic inputs when studying higher frequencies (beta and gamma), in a way dependent on the duration of the action potentials, and may introduce peaks in the coherence function that do not correspond to physiological shared inputs. Significance. This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.