Motor unit potential characterization using “pattern discovery”

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration, and regeneration: part 1, neurophysiology

Peripheral neuroregeneration research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures that can serve as biomarkers of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, such biomarkers can elucidate regeneration mechanisms and open new avenues for research. Without these measures, clinical decision-making falls short, and research becomes more costly, time-consuming, and sometimes infeasible. As a companion to Part 2, which is focused on non-invasive imaging, Part 1 of this two-part scoping review systematically identifies and critically examines many current and emerging neurophysiological techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

The motor unit and quantitative electromyography

Electromyography (EMG) assesses the anatomic motor unit (A-MU), but knowledge of its anatomy, physiology, and changes with pathology is limited. The electrophysiological motor unit (E-MU) and its motor unit potential (E-MUP) represents a fraction of the A-MU. Routine EMG assesses a limited number of E-MUP waveform characteristics (metrics) and their magnitudes qualitatively scaled in a nonlinear manner. Another approach is quantitative EMG (QEMG), whereby 20⁺ E-MUPs are extracted and both basic and derived metrics obtained and values expressed quantitatively. In diseased muscle, many E-MUP metrics may be normal, which complicates diagnostic interpretation. In QEMG, E-MUP metrics can be clustered and statistical analyses performed to assign probabilities that E-MUPs (and the muscle) are normal, neuropathic, or myopathic. In this article we review what is known about the A-MU, the restricted E-MU, E-MUP metrics, and what QEMG offers currently and in the future.

Electromyographic Findings After Epidural Steroid Injections in Patients with Radicular Low Back Pain: A Prospective Open-Label Study

Epidural steroid injections (ESIs) are commonly used in the management of chronic lower back and leg pain. The aim of this study was to investigate the short- and long-term electromyographic and clinical outcome of patients with chronic radicular pain after ESIs. This prospective, open-label study, included patients with chronic radicular pain due to disc herniation or spinal stenosis, who underwent interlaminar, fluoroscopy-guided ESIs. Patients were assessed before ESIs, as well as after 6 and 12 months, clinically (VAS 0-10, BPI, DN4, Rolland Morris, DASS, STAI) and electromyographically for the improvement of spontaneous activity (SA) and of motor unit recruitment/interference pattern (IP/MUR). A total of 39 patients were studied, 20 (51.3%) who had a significant improvement in VAS, RM, DN4 and BPI were revealed, mainly during the first 6 months (P < 0.05). Statistically significant improvement was revealed in MUR/SA for almost all nerve roots studied. Patients with disc herniation showed a greater improvement in mean difference of MUR/SA (P < 0.05) (with a prognostic value of radicular LBP versus spinal stenosis in short- [VAS P = 0.042] and long-term improvement of pain [VAS P = 0.009]. The independent variables “MUR” and “SA” had a significant prognostic value for improvement of pain (VAS: R² = 0.287, P = 0.032 and VAS: R² = 0.277, P = 0.036 respectively). Electromyographic and clinical findings indicated a benefit from epidural steroid injections. Patients with disc herniation exhibited a better outcome, especially during the first 6 months post-treatment.

Transparent muscle characterization using quantitative electromyography: different binarization mappings

Evaluation of patients with suspected neuromuscular disorders is typically based on qualitative visual and auditory assessment of needle detected eletromyographic (EMG) signals; the resulting muscle characterization is subjective and highly dependent on the skill and experience of the examiner. Quantitative electromyography (QEMG) techniques were developed to extract motor unit potential trains (MUPTs) from needle detected EMG signals, and estimate features capturing motor unit potential (MUP) morphology and quantifying morphological consistency across MUPs belonging to the same MUPT. The aim of this study is to improve available methods for obtaining transparent muscle characterizations from features obtained using QEMG techniques. More specifically, we investigate the following. 1) Can the use of binarization mappings improve muscle categorization accuracies of transparent methods? 2) What are the appropriate binarization mappings in terms of accuracy and transparency? Results from four different sets of examined limb muscles (342 muscles in total) demonstrate that four out of the 10 investigated binarization mappings based on transparent characterization methods outperformed the multi-class characterizers based on Gaussian mixture models (GMM) and the corresponding binarization mappings based on GMM. This suggests that the use of an appropriate binarization mapping can overcome the decrease in categorization accuracy associated with quantizing MUPT features, which is necessary to obtain transparent characterizations. This performance gain can be attributed to the use of more relevant features and tuned quantization to obtain more specific binary characterizations.

Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders

Different approaches have been applied for quantitative analysis of EMG signals. This study introduces the effect of Multiscale Principal Component Analysis (MSPCA) denoising method in ElectroMyoGram (EMG) signal classification. The effect of the MSPCA denoising method discussed on EMG signal classification. In addition, effect of Multiple Single Classification (MUSIC) feature extraction method presented and compared for the classification of EMG signals. The results were accomplished on the basis of EMG signal data to classify into normal, ALS or myopathic. Furthermore, total accuracy of classifiers such as k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN) and Support Vector Machines (SVMs) were discussed. Significant results were found by using MSPCA denoising method. The comparisons between the developed classifiers were based on a number of scalar performances such as sensitivity, specificity, accuracy, F-measure and area under ROC curve (AUC). The results show that MSPCA de-noising has considerably increased the accuracy as compared to EMG data without MSPCA de-noising.

Muscle categorization using PDF estimation and Naive Bayes classification

The structure of motor unit potentials (MUPs) and their times of occurrence provide information about the motor units (MUs) that created them. As such, electromyographic (EMG) data can be used to categorize muscles as normal or suffering from a neuromuscular disease. Using pattern discovery (PD) allows clinicians to understand the rationale underlying a certain muscle characterization; i.e. it is transparent. Discretization is required in PD, which leads to some loss in accuracy. In this work, characterization techniques that are based on estimating probability density functions (PDFs) for each muscle category are implemented. Characterization probabilities of each motor unit potential train (MUPT) are obtained from these PDFs and then Bayes rule is used to aggregate the MUPT characterization probabilities to calculate muscle level probabilities. Even though this technique is not as transparent as PD, its accuracy is higher than the discrete PD. Ultimately, the goal is to use a technique that is based on both PDFs and PD and make it as transparent and as efficient as possible, but first it was necessary to thoroughly assess how accurate a fully continuous approach can be. Using gaussian PDF estimation achieved improvements in muscle categorization accuracy over PD and further improvements resulted from using feature value histograms to choose more representative PDFs; for instance, using log-normal distribution to represent skewed histograms.

EMG signal decomposition using motor unit potential train validity

A system to resolve an intramuscular electromyographic (EMG) signal into its component motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters of motor units (MUs), such as their motor unit potential (MUP) templates and mean firing rates, are of interest. The system filters an EMG signal, detects MUPs, and clusters and classifies the detected MUPs into MUPTs. Clustering is partially based on the K-means algorithm, and the supervised classification is implemented using a certainty-based algorithm. Both clustering and supervised classification algorithms use MUP shape and MU firing pattern information along with signal dependent assignment criteria to obtain robust performance across a variety of EMG signals. During classification, the validity of extracted MUPTs are determined using several supervised classifiers; invalid trains are corrected and the assignment threshold for each train is adjusted based on the estimated validity (i.e., adaptive classification). Performance of the developed system in terms of accuracy (A(c)), assignment rate (A(r)), correct classification rate (CC(r)) , and the error in estimating the number of MUPTs represented in the set of detected MUPs (E(NMUPTs)) was evaluated using 32 simulated and 30 real EMG signals comprised of 3-11 and 3-15 MUPTs, respectively. The developed system, with average CC(r) of 86.4% for simulated and 96.4% for real data, outperformed a previously developed EMG decomposition system, with average CC(r) of 71.6% and 89.7% for simulated and real data, by 14.7% and 6.7%, respectively. In terms of E(NMUPTs), the new system, with average E(NMUPTs) of 0.3 and 0.2 for simulated and real data respectively, was better able to estimate the number of MUPTs represented in a set of detected MUPs than the previous system, with average E(NMUPTs) of 2.2 and 0.8 for simulated and real data respectively. For both the simulated and real data used, variations in A(c), A(r), and E(NMUPTs) for the newly developed system were lower than for the previous system, which demonstrates that the new system can successfully adjust the assignment criteria based on the characteristics of a given signal to achieve robust performance across a wide variety of EMG signals, which is of paramount importance for successfully promoting the clinical application of EMG signal decomposition techniques.

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.

Probabilistic muscle characterization using QEMG: application to neuropathic muscle

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.

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.

MUP shape-based validation of a motor unit potential train

A method using the gap statistic is proposed to evaluate the validity of a motor unit potential train (MUPT) in terms of motor unit potential (MUP) shape consistency. This algorithm determines whether the MUPs of a given MUPT are homogeneous in terms of their shapes or not. It also checks if there are gaps in the inter-discharge interval (IDI) train of the given MUPT. If the MUPs are not homogeneous or if there is a temporal gap in the MUPT, the given MUPT is split into valid trains. To overcome MUP shape variability caused by jitter or needle movement during signal detection, similar MUPTs are merged if the resulting merged train is a valid train. Experimental results using simulated EMG signals show that the accuracy of the developed method in determining valid MUPTs and invalid MUPTs correctly is 97.58% and 99.33% on average, respectively. This performance encourages the use of this method for automated validation of MUPTs.

Decision support for QEMG

For clinicians to use quantitative electromyography (QEMG) to help determine the presence or absence of neuromuscular disease, they must manually interpret an exhaustive set of motor unit potential (MUP) or interference pattern statistics to formulate a clinically useful muscle characterization. A new method is presented for automatically categorizing a set of quantitative electromyographic (EMG) data as characteristic of data acquired from a muscle affected by a myopathic, normal or neuropathic disease process, based on discovering patterns of MUP feature values. From their numbers of occurrence in a set of training data, representative of each muscle category, discovered patterns of MUP feature values are expressed as conditional probabilities of detecting such MUPs in each category of muscle. The conditional probabilities of each MUP in a set of MUPs acquired from an examined muscle are combined using Bayes' rule to estimate conditional probabilities of the examined muscle being of each category type. Using simulated and clinical data, the ability of a "pattern discovery" based Bayesian (PD-based Bayesian) method to correctly categorize sets of test MUP data was compared to conventional methods which use data means and outliers. The simulated data were created by modeling the effects of myopathic and neuropathic diseases using a physiologically based EMG signal simulator. The clinical data was from controls and patients with known neuropathic disorders. PD-based Bayesian muscle characterization had an accuracy of 84.4% compared to 51.9% for the means and outlier based method when using all MUP features considered. PD-based Bayesian methods can accurately characterize a muscle. PD-based Bayesian muscle characterization automatically maximizes both sensitivity and specificity and provides transparent rationalizations for its characterizations. This leads to the expectation that clinicians using PD-based Bayesian muscle characterization will be provided with improved decision support compared to that provided by the status quo means and outlier based methods.

Using motor unit potential characterizations to estimate neuromuscular disorder level of involvement

Based on the analysis of electromyographic (EMG) data muscles are often characterized as normal or affected by a neuromuscular disorder. Motor unit potential (MUP) characterizations comprised of the conditional probabilities of a MUP being detected from a muscle of each of the following categories: myopathic, normal, and neuropathic, were estimated. The sets of MUP characterizations of a set of MUPs detected in a muscle were averaged to produce a set of muscle characterization measures related to the probability of the muscle belonging to each category conditioned on the set of MUPs detected. Using simulated EMG signals, the objective of this work was to evaluate the correlation between the muscle characterization measures produced by different MUP characterization methods and the level of involvement of a disorder. The results showed a correlation of 0.9 between myopathic and neuropathic muscle characterization measures and the actual level of involvement when using a Pattern Discovery (PD) method to estimate MUP characterizations. This work suggests that MUP characterizations can be used to assist clinicians in tracking the progress of a disease process.